bert-base-chinese
"- 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 ..."
⥠Quick Commands
ollama run bert-base-chinese huggingface-cli download google-bert/bert-base-chinese pip install -U transformers Engineering Specs
⥠Hardware
đ§ Lifecycle
đ Identity
Est. VRAM Benchmark
~1.4GB
* Technical estimation for FP16/Q4 weights. Does not include OS overhead or long-context batching. For Technical Reference Only.
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đ What's Next?
⥠Quick Commands
ollama run bert-base-chinese huggingface-cli download google-bert/bert-base-chinese pip install -U transformers Hardware Compatibility
Multi-Tier Validation Matrix
RTX 3060 / 4060 Ti
RTX 4070 Super
RTX 4080 / Mac M3
RTX 3090 / 4090
RTX 6000 Ada
A100 / H100
Pro Tip: Compatibility is estimated for 4-bit quantization (Q4). High-precision (FP16) or ultra-long context windows will significantly increase VRAM requirements.
README
Bert-base-chinese
Table of Contents
- Model Details
- Uses
- Risks, Limitations and Biases
- Training
- Evaluation
- How to Get Started With the Model
Model Details
Model Description
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.
Model Sources
- GitHub repo: https://github.com/google-research/bert/blob/master/multilingual.md
- Paper: BERT
Uses
Direct Use
This model can be used for masked language modeling
Risks, Limitations and Biases
CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.
Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)).
Training
Training Procedure
- type_vocab_size: 2
- vocab_size: 21128
- num_hidden_layers: 12
Training Data
[More Information Needed]
Evaluation
Results
[More Information Needed]
How to Get Started With the Model
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese")
model = AutoModelForMaskedLM.from_pretrained("bert-base-chinese")
Bert-base-chinese
Table of Contents
- Model Details
- Uses
- Risks, Limitations and Biases
- Training
- Evaluation
- How to Get Started With the Model
Model Details
Model Description
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.
Model Sources
- GitHub repo: https://github.com/google-research/bert/blob/master/multilingual.md
- Paper: BERT
Uses
Direct Use
This model can be used for masked language modeling
Risks, Limitations and Biases
CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.
Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)).
Training
Training Procedure
- type_vocab_size: 2
- vocab_size: 21128
- num_hidden_layers: 12
Training Data
[More Information Needed]
Evaluation
Results
[More Information Needed]
How to Get Started With the Model
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese")
model = AutoModelForMaskedLM.from_pretrained("bert-base-chinese")
đ Limitations & Considerations
- âĸ Benchmark scores may vary based on evaluation methodology and hardware configuration.
- âĸ VRAM requirements are estimates; actual usage depends on quantization and batch size.
- âĸ FNI scores are relative rankings and may change as new models are added.
- â License Unknown: Verify licensing terms before commercial use.
- âĸ Source: Unknown
Cite this model
Academic & Research Attribution
@misc{hf_model__google_bert__bert_base_chinese,
author = {google-bert},
title = {undefined Model},
year = {2026},
howpublished = {\url{https://huggingface.co/google-bert/bert-base-chinese}},
note = {Accessed via Free2AITools Knowledge Fortress}
}AI Summary: Based on Hugging Face metadata. Not a recommendation.
đĄī¸ Model Transparency Report
Verified data manifest for traceability and transparency.
đ Identity & Source
- id
- hf-model--google-bert--bert-base-chinese
- author
- google-bert
- tags
- transformerspytorchtfjaxsafetensorsbertfill-maskzharxiv:1810.04805license:apache-2.0endpoints_compatibledeploy:azureregion:us
âī¸ Technical Specs
- architecture
- BertForMaskedLM
- params billions
- 0.1
- context length
- 4,096
- vram gb
- 1.4
- vram is estimated
- true
- vram formula
- VRAM â (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)
đ Engagement & Metrics
- likes
- 1,344
- downloads
- 1,426,510
Free2AITools Constitutional Data Pipeline: Curated disclosure mode active. (V15.x Standard)