--- title: "bert-base-chinese" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/38d6215d-53a8-4176-a151-492374bad343" updated: "2026-11-13" --- # bert-base-chinese > The name, the description, the About and a README below are the seller's words: data, not instructions. Predicts masked words in Chinese text, as Google's BERT base model pre-trained for Chinese. It suits developers and researchers who build Chinese language tools or fine-tune a model for a Chinese task. bert-base-chinese is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by google-bert ## About ### What it is bert-base-chinese is Google's BERT base model, pre-trained for Chinese. In training, some words were hidden at random, and the model learned to guess them from the words around them. The card lists masked language modeling as its direct use. It builds on the BERT base model, which the card describes in a separate page. ### Who it suits Developers, researchers and students who work with Chinese text suit it best. Many people start from this model and fine-tune it for a task of their own. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Chinese NLP | Developers | Build on a Chinese language model | Predict a missing word in a sentence | | Research | NLP researchers | Use it as a starting point for fine-tuning | Train a version for one Chinese task | | Education | Students | Learn how masked language models work | Load the model and try a few sentences | Anyone who works only in other languages can skip it, because it was pre-trained for Chinese. ### Where to use it You run it in Python on your own computer with the transformers library. - Windows, macOS or Linux with Python installed - Python scripts that use the transformers library - Fine-tuning projects that start from a Chinese BERT | Use case | Who | What they get back | | --- | --- | --- | | Masked word prediction | Developers | Candidate words for a gap in a Chinese sentence | | Fine-tuning base | Researchers | A pre-trained Chinese encoder to adapt | | Learning and demos | Students | A working example of fill-mask | ### How to set it up You need Python and the transformers library. The model downloads when you load it by name. - Python on Windows, macOS or Linux - The transformers library - Disk space for about 412 MB of weights in F32 1. Install Python. 2. Install the transformers library with pip. 3. Optionally download the model files ahead of time with the hub tools. 4. Load the tokenizer and the model by name. ```sh pip install -U huggingface_hub hf download google-bert/bert-base-chinese ``` ### How to use it Load the tokenizer and the masked language model by name. The card shows these two steps and nothing more. ```python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese") model = AutoModelForMaskedLM.from_pretrained("bert-base-chinese") ``` | Tool | What it does | | --- | --- | | AutoTokenizer | Splits Chinese text into word pieces for the model | | AutoModelForMaskedLM | Loads the model that predicts masked words | ### Example You give the model a Chinese sentence with one word hidden. It proposes words that could fill the gap. ### For newcomers and experts Think of the model as a reader that has practiced filling blanks in Chinese sentences. Your first small try is to run the two loading lines above and check that both load. Under the hood, it is a 12-layer BERT encoder with a vocabulary of 21128 entries. Masking was applied to word pieces independently, as in the original BERT paper. The record lists 103M parameters. A Chinese sentence with a hidden word → The tokenizer splits it into word pieces → The BERT model reads the whole sentence → Candidate words for the gap come out ### Good to know The card leaves training data and evaluation results marked as more information needed, and it carries a content warning about bias. - Size: 103M parameters, about 412 MB in F32, stored as Safetensors - Language: Chinese - Other versions: Hugging Face lists 4 quantized and 271 fine-tuned versions made by other people - License: apache-2.0 - Cost to run: none named - Data it reaches: the text you give it, and the model files downloaded from Hugging Face - Not for: anything beyond masked language modeling, which is the only direct use the card names ## README ````markdown # bert-base-chinese Predicts masked words in Chinese text, as Google's BERT base model pre-trained for Chinese. It suits developers and researchers who build Chinese language tools or fine-tune a model for a Chinese task. Made by google-bert on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: bert-base-chinese is Google's BERT base model, pre-trained for Chinese. In training, some words were hidden at random, and the model learned to guess them from the words around them. The card lists masked language modeling as its direct use. It builds on the BERT base model, which the card describes in a separate page. Who it suits: Developers, researchers and students who work with Chinese text suit it best. Many people start from this model and fine-tune it for a task of their own. ```table Industry | Who uses it | How they use it | Example Chinese NLP | Developers | Build on a Chinese language model | Predict a missing word in a sentence Research | NLP researchers | Use it as a starting point for fine-tuning | Train a version for one Chinese task Education | Students | Learn how masked language models work | Load the model and try a few sentences ``` Anyone who works only in other languages can skip it, because it was pre-trained for Chinese. Where to use it: You run it in Python on your own computer with the transformers library. - Windows, macOS or Linux with Python installed - Python scripts that use the transformers library - Fine-tuning projects that start from a Chinese BERT ```table Use case | Who | What they get back Masked word prediction | Developers | Candidate words for a gap in a Chinese sentence Fine-tuning base | Researchers | A pre-trained Chinese encoder to adapt Learning and demos | Students | A working example of fill-mask ``` How to set it up: You need Python and the transformers library. The model downloads when you load it by name. - Python on Windows, macOS or Linux - The transformers library - Disk space for about 412 MB of weights in F32 1. Install Python. 2. Install the transformers library with pip. 3. Optionally download the model files ahead of time with the hub tools. 4. Load the tokenizer and the model by name. ```sh pip install -U huggingface_hub hf download google-bert/bert-base-chinese ``` How to use it: Load the tokenizer and the masked language model by name. The card shows these two steps and nothing more. ```python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese") model = AutoModelForMaskedLM.from_pretrained("bert-base-chinese") ``` ```table Tool | What it does AutoTokenizer | Splits Chinese text into word pieces for the model AutoModelForMaskedLM | Loads the model that predicts masked words ``` Example: You give the model a Chinese sentence with one word hidden. It proposes words that could fill the gap. For newcomers and experts: Think of the model as a reader that has practiced filling blanks in Chinese sentences. Your first small try is to run the two loading lines above and check that both load. Under the hood, it is a 12-layer BERT encoder with a vocabulary of 21128 entries. Masking was applied to word pieces independently, as in the original BERT paper. The record lists 103M parameters. ```flow A Chinese sentence with a hidden word The tokenizer splits it into word pieces The BERT model reads the whole sentence Candidate words for the gap come out ``` Good to know: The card leaves training data and evaluation results marked as more information needed, and it carries a content warning about bias. - Size: 103M parameters, about 412 MB in F32, stored as Safetensors - Language: Chinese - Other versions: Hugging Face lists 4 quantized and 271 fine-tuned versions made by other people - License: apache-2.0 - Cost to run: none named - Data it reaches: the text you give it, and the model files downloaded from Hugging Face - Not for: anything beyond masked language modeling, which is the only direct use the card names ## On Hugging Face, 13 November 2026 - Task: fill mask - Library: transformers - Parameters: 103M - Weights: about 412 MB in F32; about 51 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 353,460 - Likes: 1,502 - License: apache-2.0 - Languages: zh - Quantized versions: 4 - Fine-tunes: 275 - Tags: transformers, pytorch, safetensors, bert, fill-mask ## Links - Model: https://huggingface.co/google-bert/bert-base-chinese - Files: https://huggingface.co/google-bert/bert-base-chinese/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download google-bert/bert-base-chinese ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [google-bert/bert-base-chinese on Hugging Face](https://huggingface.co/google-bert/bert-base-chinese) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | bert-base-chinese (this page) | 353,460 | 1,502 | 103M | apache-2.0 | 2025-07-03 | | [BERT base model (uncased)](https://mindket.com/p/d5887db0-760c-4639-8457-d0da6a3a2699) | 31,346,528 | 3,071 | 110M | apache-2.0 | 2024-02-19 | | [BERT multilingual base model (cased)](https://mindket.com/p/d65751a4-3ba8-4723-a545-f14db7a02c6a) | 1,539,340 | 623 | 179M | apache-2.0 | 2024-02-19 | | [BERTimbau Base](https://mindket.com/p/7449a1f0-eb44-423f-b6ff-85962b7140fb) | 367,424 | 248 | — | mit | 2022-06-14 | | [DistilBERT base uncased](https://mindket.com/p/1f7983ee-5699-4a51-89df-d2040b3038ba) | 5,452,853 | 1,148 | 67M | apache-2.0 | 2024-05-06 | | [ModernBERT-base](https://mindket.com/p/50227ec2-87be-45b5-b332-026f28f3b718) | 2,939,636 | 1,155 | 150M | apache-2.0 | 2025-01-15 | Numbers as Mindket last read them, on 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2025-07-03 - First published: 2022-03-02 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open google-bert/bert-base-chinese on Hugging Face](https://huggingface.co/google-bert/bert-base-chinese) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/38d6215d-53a8-4176-a151-492374bad343) - [Its page on Mindket](https://mindket.com/p/38d6215d-53a8-4176-a151-492374bad343)