There is no choice between "biased" and "not biased", everything comes with its own set of biases. Some biases are useful: Occam's Razor is an inductive bias which states that "The simplest explanation is usually the best one", which is biased in favor of elegant and simple proofs that fit available evidence with the least amount of error, and against overburdened theoretical frameworks and arguments that are unsupported by observed evidence.

What you're asking for is still a biased model, or specifically you want a language model that is biased towards reproducing the distribution of biases present in crawled web text training corpuses.

Here are some publicly available ones:

 - OpenAI GPT-3 Davinci (Commercial API only, 127 billion parameter proprietary language model trained on a corpus of web text)
 - EleutherAI GPT-NeoX 20B (a 20 billion parameter open source language model trained on The Pile)
 - Facebook AI Fairseq 13B (a 13 billion parameter open source language model trained on a corpus of web text)
 - EleutherAI GPT-J 6B (6 billion parameters, open source, The Pile)
 - OpenAI GPT-2 (A 2.7B parameter open source language model trained on a corpus of web text)
spam fails_task lang_mismatch pii not_appropriate hate_speech sexual_content quality toxicity humor helpfulness creativity violence