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")