Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between english and English. Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team. BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts 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 texts. More precisely, it was pretrained with two objectives:

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文本生成开放权重

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bert-base-cased-finetuned-mrpc

google-bert

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

填空与掩码

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bert-base-chinese

google-bert

- Model Details - Uses - Risks, Limitations and Biases - Training - Evaluation - How to Get Started With the Model This model has been pre-trained for Chinese, training and random input masking has been applied independently to word pieces (as in the original BERT paper). - **Developed by:** Google - **Model Type:** Fill-Mask - **Language(s):** Chinese - **License:** Apache 2.0 - **Parent Model:** See the BERT base uncased model for more information about the BERT base model.

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bert-base-german-cased

google-bert

**Update April 3rd, 2020**: we updated the vocabulary file on deepset's s3 to conform with the default tokenization of punctuation tokens. For details see the related FARM issue. If you want to use the old vocab we have also uploaded a "deepset/bert-base-german-cased-oldvocab" model. - We trained using Google's Tensorflow code on a single cloud TPU v2 with standard settings. - We trained 810k steps with a batch size of 1024 for sequence length 128 and 30k steps with sequence length 512. Training took about 9 days. - As training data we used the latest German Wikipedia dump (6GB of raw txt files), the OpenLegalData dump (2.4 GB) and news articles (3.6 GB). - We cleaned the data dumps with tailored scripts and segmented sentences with spacy v2.1. To create tensorflow records we used the recommended sentencepiece library for creating the word piece vocabulary and tensorflow scripts to convert the text to data usable by BERT. During training we monitored the loss and evaluated different model checkpoints on the following German datasets:

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bert-base-german-dbmdz-uncased

google-bert

This model is the same as dbmdz/bert-base-german-uncased. See the dbmdz/bert-base-german-cased model card for details on the model.

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bert-base-multilingual-cased

google-bert

Pretrained model on the top 104 languages with the largest Wikipedia using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case sensitive: it makes a difference between english and English. Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team. BERT is a transformers model pretrained on a large corpus of multilingual data in a self-supervised fashion. This means it was pretrained on the raw texts 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 texts. More precisely, it was pretrained with two objectives:

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bert-base-multilingual-uncased

google-bert

Pretrained model on the top 102 languages with the largest Wikipedia using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team. BERT is a transformers model pretrained on a large corpus of multilingual data in a self-supervised fashion. This means it was pretrained on the raw texts 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 texts. More precisely, it was pretrained with two objectives:

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bert-base-uncased

google-bert

该预训练模型基于英语，采用掩码语言建模（MLM）目标。它首次在本文中被提出，并在此代码库中首次发布。此模型不区分大小写：它不区分“english”和“English”。免责声明：发布BERT的团队未为此模型编写模型卡片，因此本模型卡片由Hugging Face团队编写。BERT是一种Transformer模型，以自监督方式在大规模英语数据语料库上进行预训练。这意味着它仅在原始文本上进行预训练，没有任何人工标注（这也是它可以使用大量公开可用数据的原因），并通过自动流程从这些文本中生成输入和标签。更准确地说，它通过两个目标进行预训练：

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bert-large-cased

google-bert

Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is cased: it makes a difference between english and English. Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team. BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts 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 texts. More precisely, it was pretrained with two objectives:

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bert-large-cased-whole-word-masking

google-bert

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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bert-large-cased-whole-word-masking-finetuned-squad

google-bert

Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is cased: it makes a difference between english and English. Differently to other BERT models, this model was trained with a new technique: Whole Word Masking. In this case, all of the tokens corresponding to a word are masked at once. The overall masking rate remains the same. The training is identical -- each masked WordPiece token is predicted independently.

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智能问答

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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。

每月输入 Token 10 M每月输出 Token 2 M1 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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bert-base-multilingual-uncased

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bert-large-cased

bert-large-cased-whole-word-masking

bert-large-cased-whole-word-masking-finetuned-squad

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好模型，也要算好成本。

读懂模型，再做选择

资讯看趋势，工具提效率，模型筑可能。