[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-2255":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":24,"defaultBranch":25,"hasWiki":24,"hasPages":24,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":28,"readmeContent":29,"aiSummary":30,"trendingCount":16,"starSnapshotCount":16,"syncStatus":31,"lastSyncTime":32,"discoverSource":33},2255,"gpt-2","openai\u002Fgpt-2","openai","Code for the paper \"Language Models are Unsupervised Multitask Learners\"","https:\u002F\u002Fopenai.com\u002Fblog\u002Fbetter-language-models\u002F",null,"Python",24926,5886,612,148,0,4,17,86,14,45,"Other",true,false,"master",[27],"paper","2026-06-12 02:00:39","**Status:** Archive (code is provided as-is, no updates expected)\n\n# gpt-2\n\nCode and models from the paper [\"Language Models are Unsupervised Multitask Learners\"](https:\u002F\u002Fd4mucfpksywv.cloudfront.net\u002Fbetter-language-models\u002Flanguage-models.pdf).\n\nYou can read about GPT-2 and its staged release in our [original blog post](https:\u002F\u002Fopenai.com\u002Fresearch\u002Fbetter-language-models\u002F), [6 month follow-up post](https:\u002F\u002Fopenai.com\u002Fblog\u002Fgpt-2-6-month-follow-up\u002F), and [final post](https:\u002F\u002Fwww.openai.com\u002Fblog\u002Fgpt-2-1-5b-release\u002F).\n\nWe have also [released a dataset](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fgpt-2-output-dataset) for researchers to study their behaviors.\n\n\u003Csup>*\u003C\u002Fsup> *Note that our original parameter counts were wrong due to an error (in our previous blog posts and paper).  Thus you may have seen small referred to as 117M and medium referred to as 345M.*\n\n## Usage\n\nThis repository is meant to be a starting point for researchers and engineers to experiment with GPT-2.\n\nFor basic information, see our [model card](.\u002Fmodel_card.md).\n\n### Some caveats\n\n- GPT-2 models' robustness and worst case behaviors are not well-understood.  As with any machine-learned model, carefully evaluate GPT-2 for your use case, especially if used without fine-tuning or in safety-critical applications where reliability is important.\n- The dataset our GPT-2 models were trained on contains many texts with [biases](https:\u002F\u002Ftwitter.com\u002FTomerUllman\u002Fstatus\u002F1101485289720242177) and factual inaccuracies, and thus GPT-2 models are likely to be biased and inaccurate as well.\n- To avoid having samples mistaken as human-written, we recommend clearly labeling samples as synthetic before wide dissemination.  Our models are often incoherent or inaccurate in subtle ways, which takes more than a quick read for a human to notice.\n\n### Work with us\n\nPlease [let us know](mailto:languagequestions@openai.com) if you’re doing interesting research with or working on applications of GPT-2!  We’re especially interested in hearing from and potentially working with those who are studying\n- Potential malicious use cases and defenses against them (e.g. the detectability of synthetic text)\n- The extent of problematic content (e.g. bias) being baked into the models and effective mitigations\n\n## Development\n\nSee [DEVELOPERS.md](.\u002FDEVELOPERS.md)\n\n## Contributors\n\nSee [CONTRIBUTORS.md](.\u002FCONTRIBUTORS.md)\n\n## Citation\n\nPlease use the following bibtex entry:\n```\n@article{radford2019language,\n  title={Language Models are Unsupervised Multitask Learners},\n  author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},\n  year={2019}\n}\n```\n\n## Future work\n\nWe may release code for evaluating the models on various benchmarks.\n\nWe are still considering release of the larger models.\n\n## License\n\n[Modified MIT](.\u002FLICENSE)\n","GPT-2是一个基于Transformer架构的自然语言处理模型，旨在展示无监督多任务学习的强大能力。该项目提供了完整的代码和预训练模型，支持研究人员和开发者进行实验与二次开发。其核心功能包括文本生成、摘要、翻译等，通过大规模语料库训练，能够生成连贯且富有上下文关联性的文本。尽管如此，用户在使用时需要注意该模型可能存在偏见及事实错误，并且在某些情况下可能表现出不可靠的行为。因此，GPT-2特别适合于非关键性应用的研究场景中，比如探索性数据分析、创意写作辅助等领域，但在安全至关重要的环境中应谨慎采用。",2,"2026-06-11 02:49:08","top_language"]