
# Bert-base-chinese

## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [How to Get Started With the Model](#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:** HuggingFace team
- **Model Type:** Fill-Mask
- **Language(s):** Chinese
- **License:** [More Information needed]
- **Parent Model:** See the [BERT base uncased model](https://huggingface.co/bert-base-uncased) for more information about the BERT base model.

### Model Sources
- **Paper:** [BERT](https://arxiv.org/abs/1810.04805)

## 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)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).


## 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
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese")

model = AutoModelForMaskedLM.from_pretrained("bert-base-chinese")

```






{"language": "zh"}
transformers pytorch tf jax safetensors bert fill-mask zh arxiv:1810.04805 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #zh #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #has_space #region-us 


# 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: HuggingFace team
- Model Type: Fill-Mask
- Language(s): Chinese
- License: [More Information needed]
- Parent Model: See the BERT base uncased model for more information about the BERT base model.

### Model Sources
- 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


## Evaluation

#### Results




## How to Get Started With the Model







# 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: HuggingFace team
- Model Type: Fill-Mask
- Language(s): Chinese
- License: [More Information needed]
- Parent Model: See the BERT base uncased model for more information about the BERT base model. ### Model Sources
- 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 ## Evaluation #### Results ## How to Get Started With the Model
TAGS
#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #zh #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # 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: HuggingFace team
- Model Type: Fill-Mask
- Language(s): Chinese
- License: [More Information needed]
- Parent Model: See the BERT base uncased model for more information about the BERT base model. ### Model Sources
- 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 ## Evaluation #### Results ## How to Get Started With the Model
passage: TAGS
#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #zh #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #has_space #region-us 
# 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: HuggingFace team
- Model Type: Fill-Mask
- Language(s): Chinese
- License: [More Information needed]
- Parent Model: See the BERT base uncased model for more information about the BERT base model.### Model Sources
- 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## Evaluation#### Results## How to Get Started With the Model