请详细解释BERT模型的工作原理，但回答必须使用json格式，并且必须使用markdown渲染。
user
```json
{
  "model_type": "BERT",
  "architecture": {
    "encoder": {
      "type": "Transformer",
      "description": "Consists of multiple layers of Transformer blocks. Each block includes self-attention mechanisms and feed-forward networks."
    },
    "pre_training": {
      "tasks": [
        {
          "name": "Masked Language Model (MLM)",
          "description": "15% of the tokens in each sequence are masked. The model predicts the original vocabulary ID of the masked word based on the context provided by the other, non-masked, tokens."
        },
        {
          "name": "Next Sentence Prediction (NSP)",
          "description": "The model is trained to determine if one sentence is the next sentence of the other in a pair of sentences."
        }
      ]
    },
    "training_challenges": {
      "optimization": "Bert uses the Adam optimizer with weight decay and learning rate warm-up",
      "tokenization": "WordPiece tokenization is employed to handle subtokens and the diversity of words."
    },
    "applications": [
      "Text classification",
      "Named entity recognition",
      "Question answering",
      "Text generation"
    ],
    "strength": "BERT's bidirectional nature allows it to capture context from both directions, making it highly effective in understanding context within sentences."
  }
}
```
assistant