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It states the estimated training data scale for text, highlights multilingual coverage with strong Chinese and English representation, and clarifies that the corpus is designed for a text-output model. The document lists public datasets such as Common Crawl and Stack Exchange, plus other third-party multi-domain text and code corpora processed with quality screening, deduplication, language identification, and safety filtering.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/public-summary-of-training-content-for-deepseek-v4-version-v-1-last-update-4082026/638898/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/public-summary-of-training-content-for-deepseek-v4-version-v-1-last-update-4082026/638898.png","ImageObject",300,407,{"name":92,"@type":93},"Ophelia","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-02","2026-10-22",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What model does this public summary cover, and what version is stated?","Question",{"text":112,"@type":113},"The summary covers DeepSeek-V4 and states the summary version as V 1.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What modalities and overall training-data scale are described?",{"text":117,"@type":113},"The document selects text as the modality and indicates the estimated text training-data size is more than 10 trillion tokens.",{"name":119,"@type":110,"acceptedAnswer":120},"Which public datasets are mentioned and how are they processed before training?",{"text":121,"@type":113},"It mentions Common Crawl and Stack Exchange, and describes processing steps including quality screening, deduplication, language identification, and safety filtering. 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General information  \n1. 1. Provider identification  \nProvider name and contact details:  \nAuthorised representative name and contact details:  \nHangzhou DeepSeek Artificial Intelligence Co., Ltd.Room 1201, Building Xi 1, Galaxy International Building, No. 169 North Huancheng Road, Gongshu District, Hangzhou, Zhejiang, China.  \nWe have appointed Prighter Group with its local partners as our authorised representative in the European Union. Please visit: [https://app.prighter.com/portal/17162496076](https://app.prighter.com/portal/17162496076)  \n1.2. Model identification  \nVersioned model name(s): Model dependencies:  \nDate ofplacement of the model on the Union market:  \n\n| DeepSeek-V4（including DeepSeek-V4-Pro and DeepSeek-V4-Flash.） |\n| --- |\n| None |\n| 24 April 2026 |\n\n1.3 Modalities, overall training data size and other characteristics  \n\n| Modality\u003Cbr>Select the modalities present in the training data, to the extent that they are identifiable | Training data size\u003Cbr>For each selected modality, select the range within which the estimated total training data size for that modalityfalls. Dynamic datasets may be excluded from the estimation. | Types of content\u003Cbr>For each selected modality, provide a general description ofthe type ofcontent that has been included in the training data. |\n| --- | --- | --- |\n| ☒ Text | ☐ Less than 1 billion tokens ☐ 1billion to 10 trillions tokens ☒ More than 10 trillions tokens | Publicly available internet information, licensed datasets, and diverse textual content including mathematical texts, source code, multilingual materials, long-form documents and text used for agentic and domainspecific training. |\n| ☐ Image | ☐ Less than 1 million images ☐ 1Million to1 billion images ☐ More than 1 billion images | N/A |\n| ☐ Audio | ☐ Less than 10 000 hours ☐ 10 000 to1 million hours ☐ More than 1 million hours | N/A |\n\n\n| ☐ Video | ☐ Less than 10 000 hours ☐ 10 000 to1 million hours ☐ More than 1 million hours | N/A |\n| --- | --- | --- |\n| ☐ Other | Specify the modality and for each one indicate approximate size and unit of measurement | N/A |\n\nLatest date of data acquisition/collection for model training:  \nDescription of the linguistic characteristics of the overall training data:  \nOther relevant characteristics of the overall training data:  \nAdditional comments (optional):  \nThe data used to train DeepSeek V4 includes different datasets from varying time periods, with some data collected approximately no later than May 2025.  \n\n| Multilingual, with strong coverage in Chinese and English. |\n| --- |\n| The training corpus is designed as a whole for the text output model. |\n| N/A |\n\n2. List of data sources  \n2. 1. Publicly available datasets  \nHave you used publicly available datasets to train the model?  \n☒ Yes ☐ No  \nIf yes, specify the modality(ies) of the content covered by the datasets concerned:  \nList of large publicly available datasets:  \nGeneral description of other publicly available datasets not listed above:  \nAdditional comments (optional):  \n\n| ☒ Text ☐ Image ☐ Video ☐ Audio\u003Cbr>☐ Other |\n| --- |\n| The training data includes text from Common Crawl and Stack Exchange. Prior to training, they are processed through quality screening, deduplication, language identification, and safety filtering. |\n| Other publicly available datasets include extensive multi-domain text and code corpora released by third parties through public repositories, online platforms, open data portals, and dedicated websites, including encompass long documents, scientific and technical materials, and source code. Prior to training, they are processed through quality screening, deduplication, language identification, and safety filtering. These datasets cover diverse subject areas worldwide and support multiple languages. |\n| N/A |\n\n2.2 Private non-publicly available datasets obtained from third parties  \n2.2","cbCaikcRwaXHvEgu","https://ap.wps.com/l/cbCaikcRwaXHvEgu","pdf",253321,"English","# 1. General information\n## 1.1 Provider identification\n## 1.2 Model identification\n## 1.3 Modalities, overall training data size and other characteristics\n# 2. List of data sources\n## 2.1 Publicly available datasets\n## 2.2 Private non-publicly available datasets obtained from third parties\n## 2.3 User data\n## 2.4 Synthetic data","[{\"question\":\"What model does this public summary cover, and what version is stated?\",\"answer\":\"The summary covers DeepSeek-V4 and states the summary version as V 1.\"},{\"question\":\"What modalities and overall training-data scale are described?\",\"answer\":\"The document selects text as the modality and indicates the estimated text training-data size is more than 10 trillion tokens.\"},{\"question\":\"Which public datasets are mentioned and how are they processed before training?\",\"answer\":\"It mentions Common Crawl and Stack Exchange, and describes processing steps including quality screening, deduplication, language identification, and safety filtering. It also references other third-party multi-domain text and code corpora with similar preprocessing steps.\"}]","Public Summary of Training Content for DeepSeek-V4 - Version V 1 - Last update - 4/08/2026 | PDF",1792700300,13]