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ProstT5

Rostlab

ProstT5 is a protein language model (pLM) which can translate between protein sequence and structure. ProstT5 (Protein structure-sequence T5) is based on ProtT5-XL-U50, a T5 model trained on encoding protein sequences using span corruption applied on billions of protein sequences. ProstT5 finetunes ProtT5-XL-U50 on translating between protein sequence and structure using 17M proteins with high-quality 3D structure predictions from the AlphaFoldDB. Protein structure is converted from 3D to 1D using the 3Di-tokens introduced by Foldseek. In a first step, ProstT5 learnt to represent the newly introduced 3Di-tokens by continuing the original span-denoising objective applied on 3Di- and amino acid- (AA) sequences. Only in a second step, ProstT5 was trained on translating between the two modalities. The direction of the translation is indicated by two special tokens ("\ " for translating from 3Di to AAs, “\ ” for translating from AAs to 3Di). To avoid clashes with AA tokens, 3Di-tokens were cast to lower-case (alphabets are identical otherwise). - **Developed by:** Michael Heinzinger (GitHub @mheinzinger; Twitter @HeinzingerM) - **Model type:** Encoder-decoder (T5) - **Language(s) (NLP):** Protein sequence and structure - **License:** MIT - **Finetuned from model:** ProtT5-XL-U50

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prot_bert

Rostlab

Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is trained on uppercase amino acids: it only works with capital letter amino acids. ProtBert is based on Bert model which pretrained on a large corpus of protein sequences in a self-supervised fashion. This means it was pretrained on the raw protein sequences only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those protein sequences. One important difference between our Bert model and the original Bert version is the way of dealing with sequences as separate documents. This means the Next sentence prediction is not used, as each sequence is treated as a complete document. The masking follows the original Bert training with randomly masks 15% of the amino acids in the input.

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Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is trained on uppercase amino acids: it only works with capital letter amino acids. ProtBert-BFD is based on Bert model which pretrained on a large corpus of protein sequences in a self-supervised fashion. This means it was pretrained on the raw protein sequences only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those protein sequences. One important difference between our Bert model and the original Bert version is the way of dealing with sequences as separate documents This means the Next sentence prediction is not used, as each sequence is treated as a complete document. The masking follows the original Bert training with randomly masks 15% of the amino acids in the input.

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An encoder-only, half-precision version of the ProtT5-XL-UniRef50 model. The original model and it's pretraining were introduced in this paper and first released in this repository. This model is trained on uppercase amino acids: it only works with capital letter amino acids. ProtT5-XL-UniRef50 is based on the `t5-3b` model and was pretrained on a large corpus of protein sequences in a self-supervised fashion. This means it was pretrained on the raw protein sequences only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those protein sequences. One important difference between this T5 model and the original T5 version is the denoising objective. The original T5-3B model was pretrained using a span denoising objective, while this model was pretrained with a Bart-like MLM denoising objective. The masking probability is consistent with the original T5 training by randomly masking 15% of the amino acids in the input.

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Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is trained on uppercase amino acids: it only works with capital letter amino acids. ProtT5-XL-UniRef50 is based on the `t5-3b` model and was pretrained on a large corpus of protein sequences in a self-supervised fashion. This means it was pretrained on the raw protein sequences only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those protein sequences. One important difference between this T5 model and the original T5 version is the denosing objective. The original T5-3B model was pretrained using a span denosing objective, while this model was pre-trained with a Bart-like MLM denosing objective. The masking probability is consistent with the original T5 training by randomly masking 15% of the amino acids in the input.

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COST EXPLORER

好模型，也要算好成本。

费用分析

调整月度 Token 用量，直观比较不同模型的预估 API 开支。

1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价

使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。

MODEL HANDBOOK

读懂模型，再做选择

选型与常见问题
API 价格应该怎么比较？+

先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。

开放权重，就代表可以免费商用吗？+

不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。

排行榜第一，就是最适合我的模型吗？+

榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。

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