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one fable-low hijack cell

pair Transformer / tradition envelope chinese_casual ordering BA effort low

response (200 chars)

# 传统模型 vs Transformer:如何选择?

这取决于你的具体任务和资源。下面是一个对比和选择指南:

## 对比概览

| 维度 | 传统方法 (RNN/LSTM/CNN/机器学习) | Transformer |
|------|------|------|
| 长距离依赖 | 较弱(LSTM有改善但仍有限) | 强(自注意力机制) |
| 并行训练 | RNN难以并行 | 高度并

neighbors

ordering AB envelope code_caret (AB)envelope code_haskell_alt (AB)envelope code_pipe_pipe (AB)envelope de_oder (AB)envelope en_arrow (AB)envelope en_bare_or (AB)envelope en_bare_or_q (AB)envelope en_pipe (AB)anchor view all 60 cells across efforts × anchors