Datasets:

librarian-bots
/

dataset_cards_with_metadata

datasetId stringlengths 5 121	author stringlengths 2 42	last_modified unknown	downloads int64 0 4.88M	likes int64 0 7.61k	tags sequencelengths 1 7.92k	task_categories sequencelengths 0 47 ⌀	createdAt unknown	card stringlengths 15 1.02M
huggingface/documentation-images	huggingface	"2025-03-06T21:53:10"	4,881,041	53	[ "license:cc-by-nc-sa-4.0", "size_categories:n<1K", "format:imagefolder", "modality:image", "library:datasets", "library:mlcroissant", "region:us" ]	null	"2022-03-02T23:29:22"	--- license: cc-by-nc-sa-4.0 --- ### This dataset contains images used in the documentation of HuggingFace's libraries. HF Team: Please make sure you optimize the assets before uploading them. My favorite tool for this is https://tinypng.com/.
Symato/cc	Symato	"2023-07-11T07:56:55"	3,221,559	2	[ "language:vi", "license:mit", "size_categories:1K<n<10K", "region:us" ]	null	"2023-07-06T04:14:51"	--- license: mit language: - vi size_categories: - 1K<n<10K --- # What is Symato CC? To download all WARC data from Common Crawl then filter out Vietnamese in Markdown and Plaintext format. There is 1% of Vietnamse in CC, extract all of them out should be a lot (~10TB of plaintext). ## Main contributors - https://huggingface.co./nampdn-ai - https://huggingface.co./binhvq - https://huggingface.co./th1nhng0 - https://huggingface.co./iambestfeed # Simple quality filters To make use of raw data from common crawl, you need to do filtering and deduping. Below is a simple quality filtering code for reference to write your own filters. ```sh ## Convert .parquet to .jsonl.gz mkdir -p jsonl filtered python3 parquet2jsonl.py ## Quality filter # wget https://huggingface.co./datasets/Symato/goods_vs_c4_cc_classifiers/resolve/main/fasttext_good_vs_c4_001.bin python3 filters.py jsonl/2023-14_20230401125552-20230401155552.jsonl.gz logging ``` # Disclaimer - We use content from Common Crawl as it is. Go to CC website to know more about data. - We provide simple quality filters code to make it easier for you to use data but no warranty the data quality meet everyone expectations. Modifiy ours or write your own filters in-case you need more advanced / better ones. Contact dung at symato dot xyz if you have other questions.
hf-doc-build/doc-build-dev	hf-doc-build	"2025-03-08T18:25:41"	1,703,228	4	[ "license:mit", "region:us", "documentation" ]	null	"2022-11-08T09:03:37"	--- license: mit tags: - documentation pretty_name: HF Documentation (PRs) --- This is a dataset which contains the docs from all the PRs that are updating one of the docs from https://huggingface.co./docs. It is automatically updated by this [github action](https://github.com/huggingface/doc-builder/blob/main/.github/workflows/build_pr_documentation.yml) from the [doc-buider](https://github.com/huggingface/doc-builder) repo.
opentensor/openvalidators	opentensor	"2023-09-25T14:03:34"	1,368,988	7	[ "license:mit", "size_categories:1M<n<10M", "region:us" ]	null	"2023-06-15T15:29:34"	--- license: mit viewer: False size_categories: - 1M<n<10M --- # Dataset Card for Openvalidators dataset ## Dataset Description - Repository: https://github.com/opentensor/validators - Homepage: https://bittensor.com/ ### Dataset Summary The OpenValidators dataset, created by the OpenTensor Foundation, is a continuously growing collection of data generated by the [OpenValidators](https://github.com/opentensor/validators) project in [W&B](https://wandb.ai/opentensor-dev/openvalidators/table). It contains millions of records and serves researchers, data scientists, and miners in the Bittensor network. The dataset provides information on network performance, node behaviors, and wandb run details. Researchers can gain insights and detect patterns, while data scientists can use it for training models and analysis. Miners can use the generated data to fine-tune their models and enhance their incentives in the network. The dataset's continuous updates support collaboration and innovation in decentralized computing. ### Version support and revisions This dataset is in constant evolution, so in order to facilitate data management, each data schema is versioned in a hugging face dataset branch, so legacy data can be easily retrieved. The main branch (or default revision) will always be the latest version of the dataset, following the latest schema adopted by the openvalidators. The current state of data organization is as following: - `v1.0`: All data collected from the first openvalidators schema, ranging from version `1.0.0` to `1.0.8`. - `main`: Current state of the dataset, following the latest schema adopted by the openvalidators (>= `1.1.0`). ### How to use The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The OpenValidators dataset gives you the granularity of extracting data by run_id, by OpenValidators version and by multiple OpenValidators versions. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. Downloading by run id For example, to download the data for a specific run, simply specify the corresponding OpenValidators version and the wandb run id in the format `version/raw_data/run_id.parquet`: ```python from datasets import load_dataset version = '1.1.0' # OpenValidators version run_id = '0drg98iy' # WandB run id run_id_dataset = load_dataset('opentensor/openvalidators', data_files=f'{version}/raw_data/{run_id}.parquet') ``` _Please note that only completed run_ids are included in the dataset. Runs that are still in progress will be ingested shortly after they finish._ Downloading by OpenValidators version One can also leverage the `datasets` library to download all the runs within a determined OpenValidators version. That can be useful for researchers and data enthusiasts that are looking to do analysis in a specific OpenValidators version state. ```python from datasets import load_dataset version = '1.1.0' # Openvalidators version version_dataset = load_dataset('opentensor/openvalidators', data_files=f'{version}/raw_data/') ``` Downloading by multiple OpenValidators version* Utilizing the `datasets` library, users can efficiently download runs from multiple OpenValidators versions. By accessing data from various OpenValidators versions, users can undertake downstream tasks such as data fine-tuning for mining or to perform big data analysis. ```python from datasets import load_dataset versions = ['1.1.0', '1.1.1', ...] # Desired versions for extraction data_files = [f'{version}/raw_data/' for version in versions] # Set data files directories dataset = load_dataset('opentensor/openvalidators', data_files={ 'test': data_files }) ``` Downloading legacy data using revisions* ```python from datasets import load_dataset version = '1.0.4' # OpenValidators version run_id = '0plco3n0' # WandB run id revision = 'v1.0' # Dataset revision run_id_dataset = load_dataset('opentensor/openvalidators', data_files=f'{version}/raw_data/{run_id}.parquet', revision=revision) ``` > Note: You can interact with legacy data in all the ways mentioned above, as long as your data scope is within the same revision. Analyzing metadata All the state related to the details of the wandb data ingestion can be accessed easily using pandas and hugging face datasets structure. This data contains relevant information regarding the metadata of the run, including user information, config information and ingestion state. ```python import pandas as pd version = '1.1.0' # OpenValidators version for metadata analysis df = pd.read_csv(f'hf://datasets/opentensor/openvalidators/{version}/metadata.csv') ``` ## Dataset Structure ### Data Instances versioned raw_data The data is provided as-in the wandb logs, without further preprocessing or tokenization. This data is located at `version/raw_data` where each file is a wandb run. metadata This dataset defines the current state of the wandb data ingestion by run id. ### Data Fields Raw data The versioned raw_data collected from W&B follows the following schema: - `rewards`: (float64) Reward vector for given step - `completion_times`: (float64) List of completion times for a given prompt - `completions`: (string) List of completions received for a given prompt - `_runtime`: (float64) Runtime of the event - `_timestamp`: (float64) Timestamp of the event - `name`: (string) Prompt type, e.g. 'followup', 'answer', 'augment' - `block`: (float64) Current block at given step - `gating_loss`: (float64) Gating model loss for given step - `rlhf_reward_model`: (float64) Output vector of the rlhf reward model - `relevance_filter`: (float64) Output vector of the relevance scoring reward model - `dahoas_reward_model`: (float64) Output vector of the dahoas reward model - `blacklist_filter`:(float64) Output vector of the blacklist filter - `nsfw_filter`:(float64) Output vector of the nsfw filter - `prompt_reward_model`:(float64) Output vector of the prompt reward model - `reciprocate_reward_model`:(float64) Output vector of the reciprocate reward model - `diversity_reward_model`:(float64) Output vector of the diversity reward model - `set_weights`: (float64) Output vector of the set weights - `uids`:(int64) Queried uids - `_step`: (int64) Step of the event - `prompt`: (string) Prompt text string - `step_length`: (float64) Elapsed time between the beginning of a run step to the end of a run step - `best`: (string) Best completion for given prompt Metadata - `run_id`: (string) Wandb Run Id - `completed`: (boolean) Flag indicating if the run_id is completed (finished, crashed or killed) - `downloaded`: (boolean) Flag indicating if the run_id data has been downloaded - `last_checkpoint`: (string) Last checkpoint of the run_id - `hotkey`: (string) Hotkey associated with the run_id - `openvalidators_version`: (string) Version of OpenValidators associated with the run_id - `problematic`: (boolean) Flag indicating if the run_id data had problems to be ingested - `problematic_reason`: (string) Reason for the run_id being problematic (Exception message) - `wandb_json_config`: (string) JSON configuration associated with the run_id in Wandb - `wandb_run_name`: (string) Name of the Wandb run - `wandb_user_info`: (string) Username information associated with the Wandb run - `wandb_tags`: (list) List of tags associated with the Wandb run - `wandb_createdAt`: (string) Timestamp of the run creation in Wandb ## Dataset Creation ### Curation Rationale This dataset was curated to provide a comprehensive and reliable collection of historical data obtained by the execution of different OpenValidators in the bittensor network. The goal is to support researchers, data scientists and developers with data generated in the network, facilitating the discovery of new insights, network analysis, troubleshooting, and data extraction for downstream tasks like mining. ### Source Data #### Initial Data Collection and Normalization The initial data collection process for this dataset involves recurrent collection by a specialized worker responsible for extracting data from wandb and ingesting it into the Hugging Face datasets structure. The collected data is organized based on the OpenValidators version and run ID to facilitate efficient data management and granular access. Each run is collected based on its corresponding OpenValidators version tag and grouped into version-specific folders. Within each version folder, a `metadata.csv` file is included to manage the collection state, while the raw data of each run is saved in the `.parquet` format with the file name corresponding to the run ID (e.g., `run_id.parquet`). Please note that the code for this data collection process will be released for transparency and reproducibility. #### Who are the source language producers? The language producers for this dataset are all the openvalidators that are logging their data into wandb in conjunction of other nodes of the bittensor network. The main wandb page where the data is sent can be accessed at https://wandb.ai/opentensor-dev/openvalidators/table. ### Licensing Information The dataset is licensed under the [MIT License](https://github.com/opentensor/validators/blob/main/LICENSE) ### Supported Tasks and Leaderboards [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
hf-doc-build/doc-build	hf-doc-build	"2025-03-08T16:50:23"	1,270,404	8	[ "license:mit", "region:us" ]	null	"2022-10-24T15:39:05"	--- license: mit pretty_name: Generated Docs for HF --- This repo contains all the docs published on https://huggingface.co./docs. The docs are generated with https://github.com/huggingface/doc-builder. <!-- comment to trigger webhook.= -->
m-a-p/FineFineWeb	m-a-p	"2024-12-19T11:34:03"	1,192,534	38	[ "task_categories:text-classification", "task_categories:text2text-generation", "task_categories:text-generation", "language:en", "license:apache-2.0", "size_categories:1B<n<10B", "modality:tabular", "modality:text", "region:us" ]	[ "text-classification", "text2text-generation", "text-generation" ]	"2024-12-14T12:46:33"	--- license: apache-2.0 task_categories: - text-classification - text2text-generation - text-generation language: - en size_categories: - n>1T --- # FineFineWeb: A Comprehensive Study on Fine-Grained Domain Web Corpus arXiv: Coming Soon Project Page: Coming Soon Blog: Coming Soon ## Data Statistics \| Domain (#tokens/#samples) \| Iteration 1 Tokens \| Iteration 2 Tokens \| Iteration 3 Tokens \| Total Tokens \| Iteration 1 Count \| Iteration 2 Count \| Iteration 3 Count \| Total Count \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| aerospace \| 5.77B \| 261.63M \| 309.33M \| 6.34B \| 9100000 \| 688505 \| 611034 \| 10399539 \| \| agronomy \| 13.08B \| 947.41M \| 229.04M \| 14.26B \| 15752828 \| 2711790 \| 649404 \| 19114022 \| \| artistic \| 178.25B \| 5.79B \| 3.75B \| 187.80B \| 314279703 \| 16113512 \| 9957104 \| 340350319 \| \| astronomy \| 5.20B \| 134.39M \| 54.66M \| 5.38B \| 7596521 \| 357647 \| 145832 \| 8100000 \| \| atmospheric_science \| 2.80B \| 102.04M \| 259.25M \| 3.16B \| 5709537 \| 267789 \| 525969 \| 6503295 \| \| automotive \| 36.72B \| 436.34M \| 911.65M \| 38.07B \| 60239679 \| 1166729 \| 1535882 \| 62942290 \| \| beauty \| 19.10B \| 671.88M \| 1.01B \| 20.78B \| 34787376 \| 1808382 \| 2201810 \| 38797568 \| \| biology \| 85.84B \| 371.29M \| 776.99M \| 86.99B \| 81413569 \| 995384 \| 1350348 \| 83759301 \| \| celebrity \| 9.63B \| 706.41M \| 4.22B \| 14.56B \| 19831188 \| 1803788 \| 7949240 \| 29584216 \| \| chemistry \| 27.80B \| 588.92M \| 131.46M \| 28.52B \| 31188189 \| 1499085 \| 328038 \| 33015312 \| \| christianity \| 47.72B \| 403.68M \| 732.55M \| 48.86B \| 55013147 \| 1349874 \| 2021458 \| 58384479 \| \| civil_engineering \| 8.85B \| 1.27B \| 402.91M \| 10.52B \| 13591632 \| 2683940 \| 940742 \| 17216314 \| \| communication_engineering \| 9.21B \| 3.60B \| 327.66M \| 13.14B \| 13001767 \| 5959526 \| 746495 \| 19707788 \| \| computer_science_and_technology \| 194.46B \| 3.95B \| 4.76B \| 203.16B \| 278420434 \| 10263521 \| 8654255 \| 297338210 \| \| design \| 96.58B \| 3.80B \| 450.00M \| 100.82B \| 190275603 \| 16653588 \| 2090515 \| 209019706 \| \| drama_and_film \| 19.12B \| 10.86B \| 206.27M \| 30.19B \| 33117478 \| 18443259 \| 564251 \| 52124988 \| \| economics \| 205.01B \| 1.23B \| 2.63B \| 208.87B \| 263965085 \| 3874091 \| 5505880 \| 273345056 \| \| electronic_science \| 30.19B \| 7.76B \| 482.62M \| 38.43B \| 42745767 \| 12572747 \| 1115605 \| 56434119 \| \| entertainment \| 152.92B \| 1.67B \| 5.06B \| 159.65B \| 256935144 \| 5801081 \| 9648023 \| 272384248 \| \| environmental_science \| 56.98B \| 1.48B \| 920.77M \| 59.37B \| 84500393 \| 3557056 \| 1966731 \| 90024180 \| \| fashion \| 18.72B \| 977.27M \| 264.01M \| 19.96B \| 53465628 \| 3926500 \| 1346988 \| 58739116 \| \| finance \| 146.39B \| 327.45M \| 1.13B \| 147.85B \| 187797764 \| 1295893 \| 3058801 \| 192152458 \| \| food \| 56.10B \| 136.32M \| 978.91M \| 57.22B \| 96485838 \| 613875 \| 3051981 \| 100151694 \| \| gamble \| 30.12B \| 696.52M \| 158.48M \| 30.98B \| 24909037 \| 770540 \| 164168 \| 25843745 \| \| game \| 43.47B \| 2.36B \| 2.68B \| 48.51B \| 65680699 \| 4670033 \| 3720700 \| 74071432 \| \| geography \| 110.18B \| 1.16B \| 192.67M \| 111.53B \| 161677214 \| 3835932 \| 559447 \| 166072593 \| \| health \| 191.20B \| 427.93M \| 18.43B \| 210.06B \| 215747152 \| 1291215 \| 23975955 \| 241014322 \| \| history \| 45.27B \| 1.56B \| 1.69B \| 48.52B \| 55710432 \| 4167508 \| 3463033 \| 63340973 \| \| hobby \| 150.23B \| 42.78B \| 44.05B \| 237.06B \| 276636362 \| 81360893 \| 71407735 \| 429404990 \| \| hydraulic_engineering \| 57.36M \| 75.40M \| 3.65M \| 136.41M \| 135079 \| 163299 \| 13453 \| 311831 \| \| instrument_science \| 5.35B \| 2.02B \| 165.43M \| 7.54B \| 8307736 \| 2904274 \| 462256 \| 11674266 \| \| journalism_and_media_communication \| 440.98B \| 21.00B \| 1.55B \| 463.53B \| 645801807 \| 50657668 \| 4909008 \| 701368483 \| \| landscape_architecture \| 3.07B \| 557.66M \| 64.76M \| 3.70B \| 5613141 \| 1138409 \| 166526 \| 6918076 \| \| law \| 128.58B \| 455.19M \| 2.38B \| 131.42B \| 166473205 \| 1660944 \| 6145032 \| 174279181 \| \| library \| 57.16B \| 5.01B \| 36.56M \| 62.21B \| 86592305 \| 10440991 \| 153014 \| 97186310 \| \| literature \| 71.07B \| 7.01B \| 67.53B \| 145.61B \| 71191075 \| 13247806 \| 54760578 \| 139199459 \| \| materials_science \| 17.79B \| 1.11B \| 303.66M \| 19.20B \| 22136519 \| 1663376 \| 708384 \| 24508279 \| \| mathematics \| 5.87B \| 50.33M \| 261.65M \| 6.18B \| 10131933 \| 179592 \| 653050 \| 10964575 \| \| mechanical_engineering \| 86.13B \| 1.24B \| 129.96M \| 87.49B \| 111778813 \| 3201605 \| 428714 \| 115409132 \| \| medical \| 140.03B \| 813.46M \| 4.97B \| 145.81B \| 149594634 \| 2266477 \| 8527901 \| 160389012 \| \| mining_engineering \| 7.26B \| 206.05M \| 529.02M \| 8.00B \| 5540631 \| 236145 \| 468458 \| 6245234 \| \| movie \| 13.09B \| 639.20M \| 124.67M \| 13.86B \| 22938808 \| 1577576 \| 511882 \| 25028266 \| \| music_and_dance \| 15.42B \| 10.38B \| 618.46M \| 26.42B \| 29566554 \| 20233446 \| 1998272 \| 51798272 \| \| news \| 328.47B \| 12.37B \| 11.34B \| 352.18B \| 508567768 \| 33206709 \| 23482422 \| 565256899 \| \| nuclear_science \| 559.05M \| 79.89M \| 78.79M \| 717.72M \| 784847 \| 170282 \| 133598 \| 1088727 \| \| ocean_science \| 2.36B \| 537.82M \| 229.43M \| 3.13B \| 3700000 \| 853052 \| 425792 \| 4978844 \| \| optical_engineering \| 2.33B \| 253.06M \| 263.99M \| 2.85B \| 3510836 \| 535026 \| 400371 \| 4446233 \| \| painting \| 374.41M \| 429.63M \| 96.57M \| 900.61M \| 875783 \| 824217 \| 336203 \| 2036203 \| \| pet \| 12.12B \| 154.14M \| 307.28M \| 12.58B \| 19624688 \| 457635 \| 778970 \| 20861293 \| \| petroleum_and_natural_gas_engineering \| 950.08M \| 515.05M \| 121.56M \| 1.59B \| 1669447 \| 899860 \| 237843 \| 2807150 \| \| philosophy \| 47.99B \| 121.26M \| 335.77M \| 48.44B \| 50396964 \| 505275 \| 1030405 \| 51932644 \| \| photo \| 6.56B \| 1.74B \| 41.44M \| 8.34B \| 16194329 \| 3901598 \| 179607 \| 20275534 \| \| physics \| 21.56B \| 372.21M \| 191.17M \| 22.12B \| 24640373 \| 843508 \| 473758 \| 25957639 \| \| politics \| 79.52B \| 253.26M \| 930.96M \| 80.70B \| 97403603 \| 1026315 \| 2504127 \| 100934045 \| \| psychology \| 51.53B \| 688.50M \| 2.56B \| 54.78B \| 58829917 \| 1881452 \| 4066667 \| 64778036 \| \| public_administration \| 100.13B \| 5.54B \| 716.81M \| 106.39B \| 160247751 \| 10657768 \| 1785347 \| 172690866 \| \| relationship \| 21.87B \| 3.69B \| 129.60M \| 25.69B \| 28153321 \| 6794774 \| 321268 \| 35269363 \| \| sociology \| 76.34B \| 3.59B \| 8.88B \| 88.82B \| 106447186 \| 7836896 \| 13040695 \| 127324777 \| \| sports \| 118.64B \| 379.18M \| 1.79B \| 120.80B \| 173243631 \| 1286718 \| 4212540 \| 178742889 \| \| statistics \| 19.59B \| 1.15B \| 1.75B \| 22.49B \| 29958726 \| 2746797 \| 3390606 \| 36096129 \| \| systems_science \| 24.58B \| 11.30B \| 163.99M \| 36.05B \| 32879249 \| 15120751 \| 470001 \| 48470001 \| \| textile_science \| 2.59B \| 2.89B \| 94.56M \| 5.57B \| 8018141 \| 8022001 \| 456668 \| 16496810 \| \| topicality \| 34.87M \| 5.22M \| 0 \| 40.09M \| 137789 \| 13506 \| 0 \| 151295 \| \| transportation_engineering \| 12.80B \| 6.61B \| 972.50M \| 20.38B \| 23595624 \| 11005933 \| 2027812 \| 36629369 \| \| travel \| 78.87B \| 584.78M \| 957.26M \| 80.41B \| 127250195 \| 1851342 \| 2430704 \| 131532241 \| \| urban_planning \| 12.13B \| 2.93B \| 53.24M \| 15.12B \| 20040937 \| 6176104 \| 201963 \| 26419004 \| \| weapons_science \| 80.62M \| 3.32B \| 140.89M \| 3.54B \| 215544 \| 5695154 \| 369541 \| 6280239 \| \| Grand Total \| 4010.76B \| 206.51B \| 208.02B \| 4425.30B \| 5781764055 \| 442387964 \| 311920860 \| 6536072879 \| ## Data Construction Workflow ![finefineweb-data-workflow](./assets/finefineweb-data-workflow.png) The data construction workflow can be summarized as follows: 1. Deduplicate: The FineWeb dataset is deduplicated using exact deduplication and MinHash techniques to remove redundant data. 2. URL Labeling: Root URLs from FineWeb are counted, and the top 1 million URLs are labeled using GPT-4. This step generates DoI (Domain-of-Interest) Coarse-Grained URLs and DoNI (Domain-of-Non-Interest) Coarse-Grained URLs as seed data sources. 3. Coarse Recall: a. Based on the labeled root URLs, data is sampled for each domain. b. The sampled data is labeled using Qwen2-7B-Instruct, producing 500K DoI Positive Data and 500K DoI Negative Data (note that for N>1 iterations, each 500K samples are composed of 250K sampled original seed data and 250K refined data after Fine Recall). c. A binary FastText model is trained per domain using the labeled data. d. The FastText model performs coarse recall on FineWeb, generating Coarse DoI Data. 4. Fine Recall: a. The Coarse DoI Data is labeled using Qwen2-72B-Instruct to produce 100K DoI Positive Data and 50K DoI Negative Data, with the latter further augmented with 50K negative samples from earlier FastText training. b. A BERT model is trained using this labeled data. c. The BERT model performs fine recall on the Coarse DoI Data, producing a refined dataset, which is the DoI subset of FineFineWeb. 5. Coarse-Fine Recall Iteration: The workflow of coarse and fine recall iterates for 3 rounds with the following adjustments: a. FastText is re-trained using updated seed data, which combines BERT-recalled samples, BERT-dropped samples, and previously labeled seed data. b. The BERT model keeps frozen during subsequent iterations. c. Steps for training FastText, coarse recall, and fine recall are repeated without re-labeling data with Qwen2-Instruct models. ## Domain-Domain Similarity Analysis 1. Perform proportional weighted sampling of the domain subsets based on the sample size of each domain, with a total of 1 billion tokens sampled from the domain subsets. 2. Use the BGE-M3 model to compute the embeddings of the samples in each domain subset, referred to as domain embeddings. 3. Use the BGE-M3 model to compute the embeddings of the samples in each benchmark, referred to as benchmark embeddings (bench embeddings). 4. Calculate the MMD distance and the Wasserstein distance between the domain embeddings and the benchmark embeddings. ![domain-benchmark similarity](./assets/domain-benchmark%20similarity.png) The results above reveal the following observations: 1. The two code-related benchmarks, MBPP and HumanEval, exhibit relatively large distances from nearly all domains, indicating that the proportion of code data in the training set is relatively small. Notably, their distance to the mathematics domain is comparatively smaller, suggesting a certain degree of overlap between mathematics data and code data. 2. Benchmarks such as Hellaswag, ARC, MMLU, and BoolQ have distances that are close to almost all domains, except for the gamble domain. This indicates that the samples in these benchmarks involve synergetic effects across multiple domains of knowledge, with a wide distribution. 3. GSM8K and TriviaQA show significant discrepancies with a small number of domains, suggesting that the distribution differences between domains are more pronounced for samples involving grade-school mathematics and fact-based question answering. Some domains contain a substantial amount of this type of data, while others do not. 4. The gamble domain exhibits substantial differences from other domains and has large distances from all benchmarks, indicating that pretraining data related to gambling provides limited benefits for these benchmarks. ## Domain-Domain Duplication Let \\(D_1, D_2, \dots, D_N\\) represent \\(N\\) distinct domains, where we select top-20 URLs for each domain \\(D_i\\), denoted as \\(\{U_{i1}, U_{i2}, \dots, U_{i20}\}\\),. The total set of URLs across all domains is represented as \\(\mathcal{U}\\), and the total number of URLs is \\(M = \|\mathcal{U}\|\\). For each URL \\(U_k \in \mathcal{U}\\), the term frequency (TF) is defined as the proportion of \\(U_k\\) in the total set of URLs: \\(\text{TF}(U_k) = \frac{\text{count}(U_k)}{M}\\) where \\(\text{count}(U_k)\\) is the number of times \\(U_k\\) appears in \\(\mathcal{U}\\). Additionally, the document frequency \\(K_k\\) of \\(U_k\\) is the number of domains in which \\(U_k\\) appears. Based on this, the inverse document frequency (IDF) is calculated as: \\(\text{IDF}(U_k) = \log(\frac{N}{K_k})\\) The TF-IDF value for each URL \\(U_{ij}\\) in a specific domain \\(D_i\\) is then computed as: \\(\text{TF-IDF}(U_{ij}) = \text{TF}(U_{ij}) \times \text{IDF}(U_{ij})\\) ![domain-domain URL duplication](./assets/duplication.png) Using the TF-IDF values of all URLs within a domain, the domain-domain duplicate rate can be analyzed by comparing the distribution of TF-IDF values across domains. If a domain has many URLs with high TF-IDF values, it indicates that the domain’s URLs are relatively unique and significant within the entire set of URLs. Conversely, if a domain has many URLs with low TF-IDF values, it suggests that the domain's URLs are more common across other domains. Analyzing these values helps assess how similar or redundant a domain's content is in relation to others based on its URL composition. As shown in the figure, most domains have low duplication rates, except for topicality, pet, and atmospheric science. ## Domain-Benchmark BPC-Acc Correlation Experimental method: Using 28 models (see the paper), we first calculate BPC for all domains to obtain a model ranking \\(R_D\\). Similarly, we compute scores across all benchmarks to obtain a model ranking \\(R_M\\). We then calculate the Spearman correlation between \\(R_D\\) and \\(R_M\\). ![domain-benchmark BPC-Acc correlation](./assets/domain-benchmark%20correlation.png) - For benchmarks like ARC, MMLU, GSM8K, HumanEval, and MBPP, STEM-related domains show higher correlation rankings, particularly mathematics, physics, and systems science. - For TriviaQA, which emphasizes factual knowledge over reasoning, domains rich in world knowledge such as literature, history, and library science demonstrate higher correlation rankings. ## Bibtex ```bibtex @misc{ title={FineFineWeb: A Comprehensive Study on Fine-grained Domain Web Corpus}, url={[https://huggingface.co./datasets/m-a-p/FineFineWeb](https://huggingface.co./datasets/m-a-p/FineFineWeb)}, author = {M-A-P, Ge Zhang, Xinrun Du, Zhimiao Yu, Zili Wang, Zekun Wang, Shuyue Guo, Tianyu Zheng, Kang Zhu, Jerry Liu, Shawn Yue, Binbin Liu, Zhongyuan Peng, Yifan Yao, Jack Yang, Ziming Li, Bingni Zhang, Minghao Liu, Tianyu Liu, Yang Gao, Wenhu Chen, Xiaohuan Zhou, Qian Liu, Taifeng Wang+, Wenhao Huang+}, publisher={huggingface}, verision={v0.1.0}, month={December}, year={2024} } ```
KakologArchives/KakologArchives	KakologArchives	"2025-03-09T01:26:46"	1,021,551	15	[ "task_categories:text-classification", "language:ja", "license:mit", "region:us" ]	[ "text-classification" ]	"2023-05-12T13:31:56"	--- pretty_name: ニコニコ実況過去ログアーカイブ license: mit language: - ja task_categories: - text-classification --- # ニコニコ実況過去ログアーカイブニコニコ実況過去ログアーカイブは、[ニコニコ実況](https://jk.nicovideo.jp) のサービス開始から現在までのすべての過去ログコメントを収集したデータセットです。去る2020年12月、ニコニコ実況は [ニコニコ生放送内の一公式チャンネルとしてリニューアル](https://blog.nicovideo.jp/niconews/143148.html) されました。これに伴い、2009年11月から運用されてきた旧システムは提供終了となり（事実上のサービス終了）、torne や BRAVIA などの家電への対応が軒並み終了する中、当時の生の声が詰まった約11年分の過去ログも同時に失われることとなってしまいました。そこで 5ch の DTV 板の住民が中心となり、旧ニコニコ実況が終了するまでに11年分の全チャンネルの過去ログをアーカイブする計画が立ち上がりました。紆余曲折あり Nekopanda 氏が約11年分のラジオや BS も含めた全チャンネルの過去ログを完璧に取得してくださったおかげで、11年分の過去ログが電子の海に消えていく事態は回避できました。しかし、旧 API が廃止されてしまったため過去ログを API 経由で取得することができなくなり、またアーカイブされた過去ログから見たい範囲のログを探す場合も、アーカイブのサイズが合計約 150GB もあることから、とても以前のように手軽に過去ログに触れることはできなくなってしまいました。一方、ニコニコ生放送内の一公式チャンネルとして移行した新ニコニコ実況では、タイムシフト（旧ニコニコ実況での過去ログに相当）の視聴期限は3週間までとなっているため、その期限を過ぎると過去ログは視聴できなくなってしまいます。また一般会員は事前にタイムシフト予約をしておく必要があるなど、以前のような利便性は失われています。私たちは、ニコニコ実況に投稿された日本のテレビ放送についてのコメントは、当時の世相や時代背景を端的に表す、歴史的価値のある資料だと考えています。このデータセットでは、ニコニコ実況のすべての過去ログを後世に残すべく、Nekopanda 氏が配布されていた旧ニコニコ実況の 2020/12/15 までのすべての過去ログに加え、コミュニティでの実況番組も含めた新ニコニコ実況、さらに 2024/06/10 からは実況用代替コメントサーバーである [NX-Jikkyo](https://nx-jikkyo.tsukumijima.net/) の当日分の過去ログを5分に1回収集し、随時反映しています。過去ログをかんたんに取得するための [API](https://jikkyo.tsukumijima.net/) もあります。よろしければそちらもご活用ください。 ## Dataset Structure ### Builder Config \| Key \| Value Type \| Default Value \| Description \| \| --------------- \| ---------- \| ------------- \| ----------- \| \| channel_id \| string \| None \| 過去ログを取得するニコニコ実況チャンネルの ID (省略時はすべてのチャンネル) \| \| year \| int \| None \| 取得する過去ログの年 (省略時はすべての年) \| \| number_of_files \| int \| None \| 取得する過去ログファイルの数 (省略時はすべてのファイル) \| ### Data Splits \| Split \| Approximate Size \| Description \| \| ------- \| ---------------- \| ----------- \| \| sample \| 1GB \| サンプルとして、2022年中に投稿された TOKYO MX (ID: jk9) のすべての過去ログコメントを取得します。1GB ほどあります。 \| \| all \| 190GB \| 全チャンネル/全期間のすべての過去ログコメントを取得します。190GB 以上あるため注意してください。 \| ### Data Fields \| Field \| Type \| Description \| \| --------------- \| -------- \| ----------- \| \| thread \| string \| コメントのスレッド ID \| \| no \| int64 \| コメント番号 (コメ番) \| \| vpos \| int64 \| スレッド ID から起算したコメントの再生位置 (1/100秒) \| \| date \| int64 \| コメント投稿時間の UNIX タイムスタンプ \| \| date_usec \| int64 \| コメント投稿時間の小数点以下の時間 \| \| user_id \| string \| ユーザー ID (コマンドに 184 が指定されている場合は匿名化され、1週間ほどでシャッフルされる) \| \| mail \| string \| コメントのコマンド (184, red naka big など、省略されることもある) \| \| premium \| boolean \| コメントしたユーザーがプレミアム会員であれば True \| \| anonymity \| boolean \| 匿名コメントであれば True \| \| content \| string \| コメント本文 (AA など、まれに複数行コメントがあるので注意) \| ## Example ```python from datasets import load_dataset dataset = load_dataset('KakologArchives/KakologArchives', 'all', channel_id='jk211', year=2023, number_of_files=10) for data in dataset['train']: print(data) ``` ## Licensing Information [MIT License](https://opensource.org/license/mit/)
open-cn-llm-leaderboard/requests	open-cn-llm-leaderboard	"2025-01-21T20:23:10"	783,187	1	[ "license:apache-2.0", "region:us" ]	null	"2024-01-23T09:43:25"	--- license: apache-2.0 ---
LLM360/TxT360	LLM360	"2024-11-08T06:29:06"	618,270	224	[ "task_categories:text-generation", "language:en", "license:odc-by", "size_categories:n>1T", "region:us" ]	[ "text-generation" ]	"2024-10-03T16:04:34"	--- license: odc-by task_categories: - text-generation language: - en size_categories: - n>1T --- # TxT360: A Top-Quality LLM Pre-training Dataset Requires the Perfect Blend <center><img src="llm360_logo(1).png" alt="k2 eval table" /></center> ## We introduce TxT360 (Trillion eXtracted Text) the first dataset to globally deduplicate 99 CommonCrawl snapshots and 14 commonly used non-web data sources (e.g. FreeLaw, PG-19, etc.) providing pretraining teams with a recipe to easily adjust data weighting, obtain the largest high-quality open source dataset, and train the most performant models. # TxT360 Compared to Common Pretraining Datasets \| Data Source \| TxT360 \| FineWeb \| RefinedWeb \| PedPajamaV2 \| C4 \| Dolma \| RedPajamaV1 \| The Pile \| \|---------------------------\|--------\|---------\|------------\|-------------\|----\|-------\|-------------\|--------------------\| \| CommonCrawl Snapshots \| 99 \| 96 \| 90 \| 84 \| 1 \| 24 \| 5 \| 0.6% of 74 \| \| Papers \| 5 Sources \| - \| - \| - \| - \| 1 Source \| 1 Source \| 4 Sources \| \| Wikipedia \| 310+ Languages \| - \| - \| - \| - \| Included \| Included \| English Only \| \| FreeLaw \| Included \| - \| - \| - \| - \| - \| - \| Included \| \| DM Math \| Included \| - \| - \| - \| - \| - \| - \| Included \| \| USPTO \| Included \| - \| - \| - \| - \| - \| - \| Included \| \| PG-19 \| Included \| - \| - \| - \| - \| Included \| Included \| Included \| \| HackerNews \| Included \| - \| - \| - \| - \| - \| - \| Included \| \| Ubuntu IRC \| Included \| - \| - \| - \| - \| - \| - \| Included \| \| EuroParl \| Included \| - \| - \| - \| - \| - \| - \| Included \| \| StackExchange \| Included \| - \| - \| - \| - \| - \| - \| Included \| \| Code \| * \| - \| - \| - \| - \| Included \| Included \| Included \| * TxT360 does not include code. This decision was made due to the perceived low duplication code with other sources. Complete details on the dataset can be found in our blog post [here](https://huggingface.co./spaces/LLM360/TxT360). ## TxT360 Performance To evaluate the training efficiency of our dataset, we sampled 1.5T tokens from both FineWeb and TxT360 (using the aforementioned weighting) and conducted a training ablation on an 8x8B Mixture-of-Experts architecture, similar to Mixtral. We compared the learning curves by tracking training loss, validation scores, and performance across a wide array of diverse evaluation benchmarks. The validation set was sampled independently from SlimPajama. Note that this experiment is done on a slightly earlier version of the dataset. <center><img src="txttofineweb.png" alt="comparison" /></center> ## Initial Data Representation To produce TxT360, a comprehensive data processing pipeline was designed to account for the nuances of both web and curated datasets. The pipeline presents a unified framework for processing both data types, making it convenient and easily adaptive for users to revise and fine-tune the pipeline for their own use cases. Web datasets are inherently noisy and varied. The TxT360 pipeline implements sophisticated filtering and deduplication techniques to clean and remove redundancies while preserving data integrity. Curated datasets are typically structured and consistently formatted, but also can cause troubles with their own special formatting preferences. TxT360 filters these sources with selective steps to maintain their integrity while providing seamless integration into the larger dataset. Both data source types are globally deduplicated together resulting in ~5T tokens of high-quality data. The table below shows the source distribution of TxT360 tokens. We further highlight the importance of mixing the datasets together with the right blend. The raw distribution of the deduplicated dataset is actually suboptimal, a simple working recipe is provided in the studies section. This recipe will create a dataset of 15T+ tokens, the largest high quality open source pre-training dataset. \| Data Source \| Raw Data Size \| Token Count \| Information Cut-Off Date \| \|-----------------\|---------------\|-------------\|--------------------------\| \| CommonCrawl \| 9.2 TB \| 4.83T \| 2024-30 \| \| Papers \| 712 GB \| 154.96B \| Q4 2023 \| \| Wikipedia \| 199 GB \| 35.975B \| - \| \| Freelaw \| 71 GB \| 16.7B \| Q1 2024 \| \| DM Math \| 22 GB \| 5.23B \| - \| \| USPTO \| 45 GB \| 4.95B \| Q3 2024 \| \| PG-19 \| 11 GB \| 2.63B \| - \| \| HackerNews \| 4.2 GB \| 1.05B \| Q4 2023 \| \| Ubuntu IRC \| 6 GB \| 1.89B \| Q3 2024 \| \| Europarl \| 6.1 GB \| 1.96B \| - \| \| StackExchange \| 81 GB \| 27.76B \| Q4 2023 \| The [TxT360](https://huggingface.co./spaces/LLM360/TxT360) blog post provides all the details behind how we approached and implemented the following features: ## CommonCrawl Data Filtering Complete discussion on how 99 Common Crawl snapshots were filtered and comparison to previous filtering techinques (e.g. Dolma, DataTrove, RedPajamaV2). ## Curated Source Filtering Each data source was filtered individually with respect to the underlying data. Full details and discussion on how each source was filter are covered. ## Global Deduplication After the web and curated sources were filtered, all sources globally deduplicated to create TxT360. The tips and tricks behind the deduplication process are included. ## Dataset Structure The dataset is organized under the ```data``` directory, with each subdirectory representing a data subset. Below is an overview of the structure and organization of these subsets: ``` ├── data ├── common-crawl # data subset ├── CC-MAIN-2013-20 # common-crawl dumps ├── 1-1 # number of duplicates ├── chunk_000_0000.jsonl.gz ├── ... ├── 2-5 ├── chunk_000_0000.jsonl.gz ├── ... ├── ... ├── CC-MAIN-2013-48 ├── 1-1 ├── chunk_000_0000.jsonl.gz ├── ... ├── ... ├── ... ├── dm_math ├── full_data_1 ├── 0_11255.jsonl ├── ... ├── full_data_2 ├── 10000_11255.jsonl ├── ... ├── arxiv ├── 1-1 # number of duplicates ├── 0_171.jsonl ├── ... ├── 2-5 ├── 0_2.jsonl ├── ... ├── ... ├── europarl ├── 1-1 # number of duplicates ├── 0_6.jsonl ├── ... ├── 2-5 ├── 0_0.jsonl ├── ... ├── ... ├── ... ``` ### Common Crawl (common-crawl) Each subdirectory under ```common-crawl``` corresponds to a specific dump of the dataset. Inside each dump folder, the data is further segmented into buckets based on the number of duplicates identified during deduplication: - ```1-1```: Contains documents with no duplicates across the dataset. - ```2-5```, ```6-10```, ```11-100```, ```101-1000```, ```1001-30000000```: Each contains documents that fall within the respective range of duplicates. Example path: ```data/common-crawl/CC-MAIN-2013-20/1-1/chunk_000_0000.jsonl.gz``` ### DM Math (dm_math) The ```dm_math``` subset is divided into two subfolders to comply with the limit of 10,000 files per folder in a HuggingFace Repository: Example path: ```data/dm_math/full_data_1/0_11255.jsonl``` ### Others Similar to common-crawl, other curated data subsets, such as arxiv, europal, etc., are organized by the number of duplicates: - ```1-1```, ```2-5```, ```6-10```, ```11-100```, ```101-1000```, ```1001-inf``` Kindly note that some data subsets might not include the folder ```1001-inf``` (```1001-30000000``` in ```common-crawl```) or might contain only a few documents in such a folder due to the rarity of documents duplicated more than 1000 times. ## Data Schema ### Common Crawl (common-crawl) The documents in common-crawl follow the schema: ```python {'text': '...', # texts in the document 'meta': { 'lang': 'en', # top 1 language detected by fastText model 'lang_score': 0.912118136882782, # language score for the detected language 'url': 'http://www.shopgirljen.com/2017/10/lg-celebrates-5-years-of-lg-oled-tv.html', # the url that raw webpage is scraped from 'timestamp': '2024-07-24T00:56:12Z', # timestamp from Common Crawl raw data 'cc-path': 'crawl-data/CC-MAIN-2024-30/segments/1720763518130.6/warc/CC-MAIN-20240723224601-20240724014601-00300.warc.gz', # the path of the document in the raw Common Crawl 'quality_signals': { 'url_score': 0.0, 'fraction_of_duplicate_lines': 0.0, 'fraction_of_characters_in_duplicate_lines': 0.0, 'fraction_of_duplicate_paragraphs': 0.0, 'fraction_of_characters_in_duplicate_paragraphs': 0.0, 'fraction_of_characters_in_most_common_ngram': [[2, 0.03626373626373627], [3, 0.03296703296703297], [4, 0.01868131868131868]], 'fraction_of_characters_in_duplicate_ngrams': [[5, 0.01868131868131868], [6, 0.01868131868131868], [7, 0.01868131868131868], [8, 0.0], [9, 0.0], [10, 0.0]], 'fraction_of_words_corrected_in_lines': 0.0, 'fraction_of_lines_ending_with_ellipsis': 0.0, 'fraction_of_lines_starting_with_bullet_point': 0.0, 'fraction_of_lines_with_toxic_words': 0.0, 'num_of_lines_with_toxic_words': 0, 'num_of_toxic_words': 0, 'word_count': 358, 'mean_word_length': 5.083798882681564, 'num_of_sentences': 19, 'symbol_to_word_ratio': 0.0, 'fraction_of_words_with_alpha_character': 1.0, 'num_of_stop_words': 82, 'num_of_paragraphs': 0, 'has_curly_bracket': False, 'has_lorem_ipsum': False, 'orig_text_has_dup_lines': False }, 'dup_signals': { 'dup_doc_count': 166, # the number of duplicated documents 'dup_dump_count': 57, # the number of dumps that the duplicated documents are from 'dup_details': # the dump distribution of the duplicated documents { '2024-30': 2, '2024-26': 1, '2024-22': 1, ... } } }, 'subset': 'commoncrawl'} ``` Please note that documents without duplicates, located in folders `/1-1/`, have an empty `dup_signals` field. Additionally, some documents with duplicates might include an `unknown` entry within the `dup_details`. One example could be: ```python {'text': '...', # texts in the document 'meta': { ... 'dup_signals': { 'dup_doc_count': 7, 'dup_dump_count': 3, 'dup_details': { 'unknown': 4, '2024-30': 1, '2024-26': 1, '2024-22': 1, } } }, 'subset': 'commoncrawl'} ``` This occurs because the distribution of duplicates across dumps was not recorded in the early stages of our deduplication process, and only the total count of duplicate documents (`dup_doc_count`) was maintained. Due to the high cost of rerunning the deduplication, we have opted to label these distributions as `unknown` when integrating them with other documents for which duplicate distribution data is available. In these cases, the `dup_dump_count` is calculated excluding the `unknown`. # Citation BibTeX:* ```bibtex @misc{txt360data2024, title={TxT360: A Top-Quality LLM Pre-training Dataset Requires the Perfect Blend}, author={Liping Tang, Nikhil Ranjan, Omkar Pangarkar, Xuezhi Liang, Zhen Wang, Li An, Bhaskar Rao, Linghao Jin, Huijuan Wang, Zhoujun Cheng, Suqi Sun, Cun Mu, Victor Miller, Xuezhe Ma, Yue Peng, Zhengzhong Liu, Eric P. Xing}, year={2024} } ```
Salesforce/GiftEvalPretrain	Salesforce	"2025-01-21T09:20:58"	603,998	3	[ "task_categories:time-series-forecasting", "license:apache-2.0", "size_categories:1M<n<10M", "modality:timeseries", "arxiv:2410.10393", "region:us", "timeseries", "forecasting", "benchmark", "gifteval" ]	[ "time-series-forecasting" ]	"2024-11-07T04:57:22"	--- license: apache-2.0 task_categories: - time-series-forecasting tags: - timeseries - forecasting - benchmark - gifteval size_categories: - 1M<n<10M --- # GIFT-Eval Pre-training Datasets Pretraining dataset aligned with [GIFT-Eval](https://huggingface.co./datasets/Salesforce/GiftEval) that has 71 univariate and 17 multivariate datasets, spanning seven domains and 13 frequencies, totaling 4.5 million time series and 230 billion data points. Notably this collection of data has no leakage issue with the train/test split and can be used to pretrain foundation models that can be fairly evaluated on GIFT-Eval. [📄 Paper](https://arxiv.org/abs/2410.10393) [🖥️ Code](https://github.com/SalesforceAIResearch/gift-eval) [📔 Blog Post]() [🏎️ Leader Board](https://huggingface.co./spaces/Salesforce/GIFT-Eval) ## Ethical Considerations This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our AUP and AI AUP. ## Citation <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> If you find this benchmark useful, please consider citing: ``` @article{aksu2024giftevalbenchmarkgeneraltime, title={GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation}, author={Taha Aksu and Gerald Woo and Juncheng Liu and Xu Liu and Chenghao Liu and Silvio Savarese and Caiming Xiong and Doyen Sahoo}, journal = {arxiv preprint arxiv:2410.10393}, year={2024}, ```
huggingface/badges	huggingface	"2024-01-19T18:27:34"	587,058	41	[ "license:mit", "size_categories:n<1K", "format:imagefolder", "modality:image", "library:datasets", "library:mlcroissant", "region:us" ]	null	"2023-02-02T14:55:23"	--- license: mit thumbnail: "https://huggingface.co./datasets/huggingface/badges/resolve/main/badges-thumbnail.png" --- <style> .prose img { display: inline; margin: 0 6px !important; } .prose table { max-width: 320px; margin: 0; } </style> # Badges A set of badges you can use anywhere. Just update the anchor URL to point to the correct action for your Space. Light or dark background with 4 sizes available: small, medium, large, and extra large. ## How to use? - With markdown, just copy the badge from: https://huggingface.co./datasets/huggingface/badges/blob/main/README.md?code=true - With HTML, inspect this page with your web browser and copy the outer html. ## Available sizes \| Small \| Medium \| Large \| Extra large \| \| ------------- \| :-----------: \| ------------- \| ------------- \| \| 20px (height) \| 24px (height) \| 36px (height) \| 48px (height) \| ## Paper page [![Paper page](https://huggingface.co./datasets/huggingface/badges/resolve/main/paper-page-sm.svg)](https://huggingface.co./papers) [![Paper page](https://huggingface.co./datasets/huggingface/badges/resolve/main/paper-page-sm-dark.svg)](https://huggingface.co./papers) [![Paper page](https://huggingface.co./datasets/huggingface/badges/resolve/main/paper-page-md.svg)](https://huggingface.co./papers) [![Paper page](https://huggingface.co./datasets/huggingface/badges/resolve/main/paper-page-md-dark.svg)](https://huggingface.co./papers) [![Paper page](https://huggingface.co./datasets/huggingface/badges/resolve/main/paper-page-lg.svg)](https://huggingface.co./papers) [![Paper page](https://huggingface.co./datasets/huggingface/badges/resolve/main/paper-page-lg-dark.svg)](https://huggingface.co./papers) [![Paper page](https://huggingface.co./datasets/huggingface/badges/resolve/main/paper-page-xl.svg)](https://huggingface.co./papers) [![Paper page](https://huggingface.co./datasets/huggingface/badges/resolve/main/paper-page-xl-dark.svg)](https://huggingface.co./papers) ## 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lavita/medical-qa-shared-task-v1-toy	lavita	"2023-07-20T00:29:06"	555,723	18	[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	null	"2023-07-20T00:28:51"	--- dataset_info: features: - name: id dtype: int64 - name: ending0 dtype: string - name: ending1 dtype: string - name: ending2 dtype: string - name: ending3 dtype: string - name: ending4 dtype: string - name: label dtype: int64 - name: sent1 dtype: string - name: sent2 dtype: string - name: startphrase dtype: string splits: - name: train num_bytes: 52480.01886421694 num_examples: 32 - name: dev num_bytes: 52490.64150943396 num_examples: 32 download_size: 89680 dataset_size: 104970.6603736509 --- # Dataset Card for "medical-qa-shared-task-v1-toy" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Salesforce/wikitext	Salesforce	"2024-01-04T16:49:18"	529,471	415	[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-sa-3.0", "license:gfdl", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:1609.07843", "region:us" ]	[ "text-generation", "fill-mask" ]	"2022-03-02T23:29:22"	--- annotations_creators: - no-annotation language_creators: - crowdsourced language: - en license: - cc-by-sa-3.0 - gfdl multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: wikitext-2 pretty_name: WikiText dataset_info: - config_name: wikitext-103-raw-v1 features: - name: text dtype: string splits: - name: test num_bytes: 1305088 num_examples: 4358 - name: train num_bytes: 546500949 num_examples: 1801350 - name: validation num_bytes: 1159288 num_examples: 3760 download_size: 315466397 dataset_size: 548965325 - config_name: wikitext-103-v1 features: - name: text dtype: string splits: - name: test num_bytes: 1295575 num_examples: 4358 - name: train num_bytes: 545141915 num_examples: 1801350 - name: validation num_bytes: 1154751 num_examples: 3760 download_size: 313093838 dataset_size: 547592241 - config_name: wikitext-2-raw-v1 features: - name: text dtype: string splits: - name: test num_bytes: 1305088 num_examples: 4358 - name: train num_bytes: 11061717 num_examples: 36718 - name: validation num_bytes: 1159288 num_examples: 3760 download_size: 7747362 dataset_size: 13526093 - config_name: wikitext-2-v1 features: - name: text dtype: string splits: - name: test num_bytes: 1270947 num_examples: 4358 - name: train num_bytes: 10918118 num_examples: 36718 - name: validation num_bytes: 1134123 num_examples: 3760 download_size: 7371282 dataset_size: 13323188 configs: - config_name: wikitext-103-raw-v1 data_files: - split: test path: wikitext-103-raw-v1/test-* - split: train path: wikitext-103-raw-v1/train-* - split: validation path: wikitext-103-raw-v1/validation-* - config_name: wikitext-103-v1 data_files: - split: test path: wikitext-103-v1/test-* - split: train path: wikitext-103-v1/train-* - split: validation path: wikitext-103-v1/validation-* - config_name: wikitext-2-raw-v1 data_files: - split: test path: wikitext-2-raw-v1/test-* - split: train path: wikitext-2-raw-v1/train-* - split: validation path: wikitext-2-raw-v1/validation-* - config_name: wikitext-2-v1 data_files: - split: test path: wikitext-2-v1/test-* - split: train path: wikitext-2-v1/train-* - split: validation path: wikitext-2-v1/validation-* --- # Dataset Card for "wikitext" ## 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://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [Pointer Sentinel Mixture Models](https://arxiv.org/abs/1609.07843) - Point of Contact: [Stephen Merity](mailto:[email protected]) - Size of downloaded dataset files: 391.41 MB - Size of the generated dataset: 1.12 GB - Total amount of disk used: 1.52 GB ### Dataset Summary The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike License. Compared to the preprocessed version of Penn Treebank (PTB), WikiText-2 is over 2 times larger and WikiText-103 is over 110 times larger. The WikiText dataset also features a far larger vocabulary and retains the original case, punctuation and numbers - all of which are removed in PTB. As it is composed of full articles, the dataset is well suited for models that can take advantage of long term dependencies. Each subset comes in two different variants: - Raw (for character level work) contain the raw tokens, before the addition of the <unk> (unknown) tokens. - Non-raw (for word level work) contain only the tokens in their vocabulary (wiki.train.tokens, wiki.valid.tokens, and wiki.test.tokens). The out-of-vocabulary tokens have been replaced with the the <unk> token. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### wikitext-103-raw-v1 - Size of downloaded dataset files: 191.98 MB - Size of the generated dataset: 549.42 MB - Total amount of disk used: 741.41 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "text": "\" The gold dollar or gold one @-@ dollar piece was a coin struck as a regular issue by the United States Bureau of the Mint from..." } ``` #### wikitext-103-v1 - Size of downloaded dataset files: 190.23 MB - Size of the generated dataset: 548.05 MB - Total amount of disk used: 738.27 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "\" Senjō no Valkyria 3 : <unk> Chronicles ( Japanese : 戦場のヴァルキュリア3 , lit . Valkyria of the Battlefield 3 ) , commonly referred to..." } ``` #### wikitext-2-raw-v1 - Size of downloaded dataset files: 4.72 MB - Size of the generated dataset: 13.54 MB - Total amount of disk used: 18.26 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "\" The Sinclair Scientific Programmable was introduced in 1975 , with the same case as the Sinclair Oxford . It was larger than t..." } ``` #### wikitext-2-v1 - Size of downloaded dataset files: 4.48 MB - Size of the generated dataset: 13.34 MB - Total amount of disk used: 17.82 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "\" Senjō no Valkyria 3 : <unk> Chronicles ( Japanese : 戦場のヴァルキュリア3 , lit . Valkyria of the Battlefield 3 ) , commonly referred to..." } ``` ### Data Fields The data fields are the same among all splits. #### wikitext-103-raw-v1 - `text`: a `string` feature. #### wikitext-103-v1 - `text`: a `string` feature. #### wikitext-2-raw-v1 - `text`: a `string` feature. #### wikitext-2-v1 - `text`: a `string` feature. ### Data Splits \| name \| train \|validation\|test\| \|-------------------\|------:\|---------:\|---:\| \|wikitext-103-raw-v1\|1801350\| 3760\|4358\| \|wikitext-103-v1 \|1801350\| 3760\|4358\| \|wikitext-2-raw-v1 \| 36718\| 3760\|4358\| \|wikitext-2-v1 \| 36718\| 3760\|4358\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The dataset is available under the [Creative Commons Attribution-ShareAlike License (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/). ### Citation Information ``` @misc{merity2016pointer, title={Pointer Sentinel Mixture Models}, author={Stephen Merity and Caiming Xiong and James Bradbury and Richard Socher}, year={2016}, eprint={1609.07843}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.
hallucinations-leaderboard/results	hallucinations-leaderboard	"2024-10-31T20:32:52"	524,187	2	[ "license:apache-2.0", "region:us" ]	null	"2023-11-21T11:44:46"	--- license: apache-2.0 ---

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Dataset Card for Hugging Face Hub Dataset Cards

This datasets consists of dataset cards for models hosted on the Hugging Face Hub. The dataset cards are created by the community and provide information about datasets hosted on the Hugging Face Hub. This dataset is updated on a daily basis and includes publicly available datasets on the Hugging Face Hub.

This dataset is made available to help support users wanting to work with a large number of Dataset Cards from the Hub. We hope that this dataset will help support research in the area of Dataset Cards and their use but the format of this dataset may not be useful for all use cases. If there are other features that you would like to see included in this dataset, please open a new discussion.

Dataset Details

Uses

There are a number of potential uses for this dataset including:

text mining to find common themes in dataset cards
analysis of the dataset card format/content
topic modelling of dataset cards
training language models on the dataset cards

Out-of-Scope Use

[More Information Needed]

Dataset Structure

This dataset has a single split.

Dataset Creation

Curation Rationale

The dataset was created to assist people in working with dataset cards. In particular it was created to support research in the area of dataset cards and their use. It is possible to use the Hugging Face Hub API or client library to download dataset cards and this option may be preferable if you have a very specific use case or require a different format.

Source Data

The source data is README.md files for datasets hosted on the Hugging Face Hub. We do not include any other supplementary files that may be included in the dataset directory.

Data Collection and Processing

The data is downloaded using a CRON job on a daily basis.

Who are the source data producers?

The source data producers are the creators of the dataset cards on the Hugging Face Hub. This includes a broad variety of people from the community ranging from large companies to individual researchers. We do not gather any information about who created the dataset card in this repository although this information can be gathered from the Hugging Face Hub API.

Annotations [optional]

There are no additional annotations in this dataset beyond the dataset card content.

Annotation process

N/A

Who are the annotators?

N/A

Personal and Sensitive Information

We make no effort to anonymize the data. Whilst we don't expect the majority of dataset cards to contain personal or sensitive information, it is possible that some dataset cards may contain this information. Dataset cards may also link to websites or email addresses.

Bias, Risks, and Limitations

Dataset cards are created by the community and we do not have any control over the content of the dataset cards. We do not review the content of the dataset cards and we do not make any claims about the accuracy of the information in the dataset cards. Some dataset cards will themselves discuss bias and sometimes this is done by providing examples of bias in either the training data or the responses provided by the dataset. As a result this dataset may contain examples of bias.

Whilst we do not directly download any images linked to in the dataset cards, some dataset cards may include images. Some of these images may not be suitable for all audiences.