diff --git a/docs/source/zh/_toctree.yml b/docs/source/zh/_toctree.yml
index 66816a53098..50123200bcb 100644
--- a/docs/source/zh/_toctree.yml
+++ b/docs/source/zh/_toctree.yml
@@ -157,5 +157,8 @@
title: 通用工具
- local: internal/time_series_utils
title: 时序数据工具
+ - sections:
+ - local: model_doc/bert
+ title: BERT
title: 内部辅助工具
- title: 应用程序接口 (API)
+ title: 应用程序接口 (API)
\ No newline at end of file
diff --git a/docs/source/zh/model_doc/bert.md b/docs/source/zh/model_doc/bert.md
new file mode 100644
index 00000000000..db61b675e49
--- /dev/null
+++ b/docs/source/zh/model_doc/bert.md
@@ -0,0 +1,258 @@
+
+
+
+
+# BERT
+
+[BERT](https://huggingface.co/papers/1810.04805) 是一个在无标签的文本数据上预训练的双向 transformer，用于预测句子中被掩码的（masked） token，以及预测一个句子是否跟随在另一个句子之后。其主要思想是，在预训练过程中，通过随机掩码一些 token，让模型利用左右上下文的信息预测它们，从而获得更全面深入的理解。此外，BERT 具有很强的通用性，其学习到的语言表示可以通过额外的层或头进行微调，从而适配其他下游 NLP 任务。
+
+你可以在 [BERT](https://huggingface.co/collections/google/bert-release-64ff5e7a4be99045d1896dbc) 集合下找到 BERT 的所有原始 checkpoint。
+
+> [!TIP]
+> 点击右侧边栏中的 BERT 模型，以查看将 BERT 应用于不同语言任务的更多示例。
+
+下面的示例演示了如何使用 [`Pipeline`], [`AutoModel`] 和命令行预测 `[MASK]` token。
+
+
+
+
+```py
+import torch
+from transformers import pipeline
+
+pipeline = pipeline(
+ task="fill-mask",
+ model="google-bert/bert-base-uncased",
+ torch_dtype=torch.float16,
+ device=0
+)
+pipeline("Plants create [MASK] through a process known as photosynthesis.")
+```
+
+
+
+
+```py
+import torch
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained(
+ "google-bert/bert-base-uncased",
+)
+model = AutoModelForMaskedLM.from_pretrained(
+ "google-bert/bert-base-uncased",
+ torch_dtype=torch.float16,
+ device_map="auto",
+ attn_implementation="sdpa"
+)
+inputs = tokenizer("Plants create [MASK] through a process known as photosynthesis.", return_tensors="pt").to("cuda")
+
+with torch.no_grad():
+ outputs = model(**inputs)
+ predictions = outputs.logits
+
+masked_index = torch.where(inputs['input_ids'] == tokenizer.mask_token_id)[1]
+predicted_token_id = predictions[0, masked_index].argmax(dim=-1)
+predicted_token = tokenizer.decode(predicted_token_id)
+
+print(f"The predicted token is: {predicted_token}")
+```
+
+
+
+
+```bash
+echo -e "Plants create [MASK] through a process known as photosynthesis." | transformers-cli run --task fill-mask --model google-bert/bert-base-uncased --device 0
+```
+
+
+
+
+## 注意
+
+- 输入内容应在右侧进行填充，因为 BERT 使用绝对位置嵌入。
+## BertConfig
+
+[[autodoc]] BertConfig
+ - all
+
+## BertTokenizer
+
+[[autodoc]] BertTokenizer
+ - build_inputs_with_special_tokens
+ - get_special_tokens_mask
+ - create_token_type_ids_from_sequences
+ - save_vocabulary
+
+## BertTokenizerFast
+
+[[autodoc]] BertTokenizerFast
+
+## BertModel
+
+[[autodoc]] BertModel
+ - forward
+
+## BertForPreTraining
+
+[[autodoc]] BertForPreTraining
+ - forward
+
+## BertLMHeadModel
+
+[[autodoc]] BertLMHeadModel
+ - forward
+
+## BertForMaskedLM
+
+[[autodoc]] BertForMaskedLM
+ - forward
+
+## BertForNextSentencePrediction
+
+[[autodoc]] BertForNextSentencePrediction
+ - forward
+
+## BertForSequenceClassification
+
+[[autodoc]] BertForSequenceClassification
+ - forward
+
+## BertForMultipleChoice
+
+[[autodoc]] BertForMultipleChoice
+ - forward
+
+## BertForTokenClassification
+
+[[autodoc]] BertForTokenClassification
+ - forward
+
+## BertForQuestionAnswering
+
+[[autodoc]] BertForQuestionAnswering
+ - forward
+
+## TFBertTokenizer
+
+[[autodoc]] TFBertTokenizer
+
+## TFBertModel
+
+[[autodoc]] TFBertModel
+ - call
+
+## TFBertForPreTraining
+
+[[autodoc]] TFBertForPreTraining
+ - call
+
+## TFBertModelLMHeadModel
+
+[[autodoc]] TFBertLMHeadModel
+ - call
+
+## TFBertForMaskedLM
+
+[[autodoc]] TFBertForMaskedLM
+ - call
+
+## TFBertForNextSentencePrediction
+
+[[autodoc]] TFBertForNextSentencePrediction
+ - call
+
+## TFBertForSequenceClassification
+
+[[autodoc]] TFBertForSequenceClassification
+ - call
+
+## TFBertForMultipleChoice
+
+[[autodoc]] TFBertForMultipleChoice
+ - call
+
+## TFBertForTokenClassification
+
+[[autodoc]] TFBertForTokenClassification
+ - call
+
+## TFBertForQuestionAnswering
+
+[[autodoc]] TFBertForQuestionAnswering
+ - call
+
+## FlaxBertModel
+
+[[autodoc]] FlaxBertModel
+ - __call__
+
+## FlaxBertForPreTraining
+
+[[autodoc]] FlaxBertForPreTraining
+ - __call__
+
+## FlaxBertForCausalLM
+
+[[autodoc]] FlaxBertForCausalLM
+ - __call__
+
+## FlaxBertForMaskedLM
+
+[[autodoc]] FlaxBertForMaskedLM
+ - __call__
+
+## FlaxBertForNextSentencePrediction
+
+[[autodoc]] FlaxBertForNextSentencePrediction
+ - __call__
+
+## FlaxBertForSequenceClassification
+
+[[autodoc]] FlaxBertForSequenceClassification
+ - __call__
+
+## FlaxBertForMultipleChoice
+
+[[autodoc]] FlaxBertForMultipleChoice
+ - __call__
+
+## FlaxBertForTokenClassification
+
+[[autodoc]] FlaxBertForTokenClassification
+ - __call__
+
+## FlaxBertForQuestionAnswering
+
+[[autodoc]] FlaxBertForQuestionAnswering
+ - __call__
+
+## Bert specific outputs
+
+[[autodoc]] models.bert.modeling_bert.BertForPreTrainingOutput
+
+[[autodoc]] models.bert.modeling_tf_bert.TFBertForPreTrainingOutput
+
+[[autodoc]] models.bert.modeling_flax_bert.FlaxBertForPreTrainingOutput
\ No newline at end of file
+ 
+ 
+ 
+