1585 lines
59 KiB
Plaintext
1585 lines
59 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "f21c7eb3",
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"metadata": {
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"origin_pos": 0
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},
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"source": [
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"# 自然语言推断：使用注意力\n",
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":label:`sec_natural-language-inference-attention`\n",
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"\n",
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"我们在 :numref:`sec_natural-language-inference-and-dataset`中介绍了自然语言推断任务和SNLI数据集。鉴于许多模型都是基于复杂而深度的架构，Parikh等人提出用注意力机制解决自然语言推断问题，并称之为“可分解注意力模型” :cite:`Parikh.Tackstrom.Das.ea.2016`。这使得模型没有循环层或卷积层，在SNLI数据集上以更少的参数实现了当时的最佳结果。本节将描述并实现这种基于注意力的自然语言推断方法（使用MLP），如 :numref:`fig_nlp-map-nli-attention`中所述。\n",
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"\n",
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"\n",
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":label:`fig_nlp-map-nli-attention`\n",
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"\n",
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"## 模型\n",
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"\n",
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"与保留前提和假设中词元的顺序相比，我们可以将一个文本序列中的词元与另一个文本序列中的每个词元对齐，然后比较和聚合这些信息，以预测前提和假设之间的逻辑关系。与机器翻译中源句和目标句之间的词元对齐类似，前提和假设之间的词元对齐可以通过注意力机制灵活地完成。\n",
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"\n",
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"\n",
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":label:`fig_nli_attention`\n",
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"\n",
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" :numref:`fig_nli_attention`描述了使用注意力机制的自然语言推断方法。从高层次上讲，它由三个联合训练的步骤组成：对齐、比较和汇总。我们将在下面一步一步地对它们进行说明。\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "012b50e0",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T06:58:26.279483Z",
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"iopub.status.busy": "2023-08-18T06:58:26.278712Z",
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"iopub.status.idle": "2023-08-18T06:58:29.526584Z",
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"shell.execute_reply": "2023-08-18T06:58:29.525381Z"
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},
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"origin_pos": 2,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [],
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"source": [
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"import torch\n",
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"from torch import nn\n",
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"from torch.nn import functional as F\n",
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"from d2l import torch as d2l"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8daac4ce",
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"metadata": {
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"origin_pos": 4
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},
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"source": [
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"### 注意（Attending）\n",
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"\n",
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"第一步是将一个文本序列中的词元与另一个序列中的每个词元对齐。假设前提是“我确实需要睡眠”，假设是“我累了”。由于语义上的相似性，我们不妨将假设中的“我”与前提中的“我”对齐，将假设中的“累”与前提中的“睡眠”对齐。同样，我们可能希望将前提中的“我”与假设中的“我”对齐，将前提中的“需要”和“睡眠”与假设中的“累”对齐。请注意，这种对齐是使用加权平均的“软”对齐，其中理想情况下较大的权重与要对齐的词元相关联。为了便于演示， :numref:`fig_nli_attention`以“硬”对齐的方式显示了这种对齐方式。\n",
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"\n",
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"现在，我们更详细地描述使用注意力机制的软对齐。用$\\mathbf{A} = (\\mathbf{a}_1, \\ldots, \\mathbf{a}_m)$和$\\mathbf{B} = (\\mathbf{b}_1, \\ldots, \\mathbf{b}_n)$表示前提和假设，其词元数量分别为$m$和$n$，其中$\\mathbf{a}_i, \\mathbf{b}_j \\in \\mathbb{R}^{d}$（$i = 1, \\ldots, m, j = 1, \\ldots, n$）是$d$维的词向量。对于软对齐，我们将注意力权重$e_{ij} \\in \\mathbb{R}$计算为：\n",
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"\n",
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"$$e_{ij} = f(\\mathbf{a}_i)^\\top f(\\mathbf{b}_j),$$\n",
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":eqlabel:`eq_nli_e`\n",
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"\n",
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"其中函数$f$是在下面的`mlp`函数中定义的多层感知机。输出维度$f$由`mlp`的`num_hiddens`参数指定。\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "03198966",
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"metadata": {
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"execution": {
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||
"iopub.execute_input": "2023-08-18T06:58:29.532436Z",
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"iopub.status.idle": "2023-08-18T06:58:29.540551Z",
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"shell.execute_reply": "2023-08-18T06:58:29.539443Z"
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},
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"origin_pos": 6,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [],
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"source": [
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"def mlp(num_inputs, num_hiddens, flatten):\n",
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" net = []\n",
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" net.append(nn.Dropout(0.2))\n",
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" net.append(nn.Linear(num_inputs, num_hiddens))\n",
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" net.append(nn.ReLU())\n",
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" if flatten:\n",
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" net.append(nn.Flatten(start_dim=1))\n",
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" net.append(nn.Dropout(0.2))\n",
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" net.append(nn.Linear(num_hiddens, num_hiddens))\n",
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" net.append(nn.ReLU())\n",
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" if flatten:\n",
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" net.append(nn.Flatten(start_dim=1))\n",
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" return nn.Sequential(*net)"
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]
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},
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{
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"cell_type": "markdown",
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||
"id": "fe5a88db",
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"metadata": {
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"origin_pos": 8
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},
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"source": [
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"值得注意的是，在 :eqref:`eq_nli_e`中，$f$分别输入$\\mathbf{a}_i$和$\\mathbf{b}_j$，而不是将它们一对放在一起作为输入。这种*分解*技巧导致$f$只有$m + n$个次计算（线性复杂度），而不是$mn$次计算（二次复杂度）\n",
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"\n",
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"对 :eqref:`eq_nli_e`中的注意力权重进行规范化，我们计算假设中所有词元向量的加权平均值，以获得假设的表示，该假设与前提中索引$i$的词元进行软对齐：\n",
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"\n",
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"$$\n",
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"\\boldsymbol{\\beta}_i = \\sum_{j=1}^{n}\\frac{\\exp(e_{ij})}{ \\sum_{k=1}^{n} \\exp(e_{ik})} \\mathbf{b}_j.\n",
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"$$\n",
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"\n",
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"同样，我们计算假设中索引为$j$的每个词元与前提词元的软对齐：\n",
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"\n",
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"$$\n",
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"\\boldsymbol{\\alpha}_j = \\sum_{i=1}^{m}\\frac{\\exp(e_{ij})}{ \\sum_{k=1}^{m} \\exp(e_{kj})} \\mathbf{a}_i.\n",
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"$$\n",
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"\n",
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"下面，我们定义`Attend`类来计算假设（`beta`）与输入前提`A`的软对齐以及前提（`alpha`）与输入假设`B`的软对齐。\n"
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]
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},
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{
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||
"cell_type": "code",
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||
"execution_count": 3,
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||
"id": "d57da2e1",
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||
"metadata": {
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||
"execution": {
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||
"iopub.execute_input": "2023-08-18T06:58:29.545456Z",
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"shell.execute_reply": "2023-08-18T06:58:29.553173Z"
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},
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||
"origin_pos": 10,
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"tab": [
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||
"pytorch"
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||
]
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||
},
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"outputs": [],
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"source": [
|
||
"class Attend(nn.Module):\n",
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" def __init__(self, num_inputs, num_hiddens, **kwargs):\n",
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" super(Attend, self).__init__(**kwargs)\n",
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" self.f = mlp(num_inputs, num_hiddens, flatten=False)\n",
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"\n",
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" def forward(self, A, B):\n",
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" # A/B的形状：（批量大小，序列A/B的词元数，embed_size）\n",
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" # f_A/f_B的形状：（批量大小，序列A/B的词元数，num_hiddens）\n",
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" f_A = self.f(A)\n",
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" f_B = self.f(B)\n",
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" # e的形状：（批量大小，序列A的词元数，序列B的词元数）\n",
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" e = torch.bmm(f_A, f_B.permute(0, 2, 1))\n",
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" # beta的形状：（批量大小，序列A的词元数，embed_size），\n",
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" # 意味着序列B被软对齐到序列A的每个词元(beta的第1个维度)\n",
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" beta = torch.bmm(F.softmax(e, dim=-1), B)\n",
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" # beta的形状：（批量大小，序列B的词元数，embed_size），\n",
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" # 意味着序列A被软对齐到序列B的每个词元(alpha的第1个维度)\n",
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" alpha = torch.bmm(F.softmax(e.permute(0, 2, 1), dim=-1), A)\n",
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||
" return beta, alpha"
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||
]
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||
},
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||
{
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||
"cell_type": "markdown",
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||
"id": "17ead0f5",
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||
"metadata": {
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||
"origin_pos": 12
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||
},
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||
"source": [
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||
"### 比较\n",
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"\n",
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"在下一步中，我们将一个序列中的词元与与该词元软对齐的另一个序列进行比较。请注意，在软对齐中，一个序列中的所有词元（尽管可能具有不同的注意力权重）将与另一个序列中的词元进行比较。为便于演示， :numref:`fig_nli_attention`对词元以*硬*的方式对齐。例如，上述的*注意*（attending）步骤确定前提中的“need”和“sleep”都与假设中的“tired”对齐，则将对“疲倦-需要睡眠”进行比较。\n",
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"\n",
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"在比较步骤中，我们将来自一个序列的词元的连结（运算符$[\\cdot, \\cdot]$）和来自另一序列的对齐的词元送入函数$g$（一个多层感知机）：\n",
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"\n",
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"$$\\mathbf{v}_{A,i} = g([\\mathbf{a}_i, \\boldsymbol{\\beta}_i]), i = 1, \\ldots, m\\\\ \\mathbf{v}_{B,j} = g([\\mathbf{b}_j, \\boldsymbol{\\alpha}_j]), j = 1, \\ldots, n.$$\n",
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"\n",
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":eqlabel:`eq_nli_v_ab`\n",
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"\n",
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"在 :eqref:`eq_nli_v_ab`中，$\\mathbf{v}_{A,i}$是指，所有假设中的词元与前提中词元$i$软对齐，再与词元$i$的比较；而$\\mathbf{v}_{B,j}$是指，所有前提中的词元与假设中词元$i$软对齐，再与词元$i$的比较。下面的`Compare`个类定义了比较步骤。\n"
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||
]
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||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
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||
"id": "3587e2bc",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2023-08-18T06:58:29.559109Z",
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"iopub.status.busy": "2023-08-18T06:58:29.558368Z",
|
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"iopub.status.idle": "2023-08-18T06:58:29.566400Z",
|
||
"shell.execute_reply": "2023-08-18T06:58:29.565225Z"
|
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},
|
||
"origin_pos": 14,
|
||
"tab": [
|
||
"pytorch"
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||
]
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||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"class Compare(nn.Module):\n",
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" def __init__(self, num_inputs, num_hiddens, **kwargs):\n",
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||
" super(Compare, self).__init__(**kwargs)\n",
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" self.g = mlp(num_inputs, num_hiddens, flatten=False)\n",
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"\n",
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" def forward(self, A, B, beta, alpha):\n",
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" V_A = self.g(torch.cat([A, beta], dim=2))\n",
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" V_B = self.g(torch.cat([B, alpha], dim=2))\n",
|
||
" return V_A, V_B"
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||
]
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||
},
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||
{
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||
"cell_type": "markdown",
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||
"id": "60dd10dd",
|
||
"metadata": {
|
||
"origin_pos": 16
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||
},
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"source": [
|
||
"### 聚合\n",
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"\n",
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"现在我们有两组比较向量$\\mathbf{v}_{A,i}$（$i = 1, \\ldots, m$）和$\\mathbf{v}_{B,j}$（$j = 1, \\ldots, n$）。在最后一步中，我们将聚合这些信息以推断逻辑关系。我们首先求和这两组比较向量：\n",
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"\n",
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"$$\n",
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"\\mathbf{v}_A = \\sum_{i=1}^{m} \\mathbf{v}_{A,i}, \\quad \\mathbf{v}_B = \\sum_{j=1}^{n}\\mathbf{v}_{B,j}.\n",
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"$$\n",
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"\n",
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"接下来，我们将两个求和结果的连结提供给函数$h$（一个多层感知机），以获得逻辑关系的分类结果：\n",
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"\n",
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"$$\n",
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"\\hat{\\mathbf{y}} = h([\\mathbf{v}_A, \\mathbf{v}_B]).\n",
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"$$\n",
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"\n",
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"聚合步骤在以下`Aggregate`类中定义。\n"
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]
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||
},
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||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
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||
"id": "24cd9b3d",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2023-08-18T06:58:29.571174Z",
|
||
"iopub.status.busy": "2023-08-18T06:58:29.570475Z",
|
||
"iopub.status.idle": "2023-08-18T06:58:29.578900Z",
|
||
"shell.execute_reply": "2023-08-18T06:58:29.577743Z"
|
||
},
|
||
"origin_pos": 18,
|
||
"tab": [
|
||
"pytorch"
|
||
]
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"class Aggregate(nn.Module):\n",
|
||
" def __init__(self, num_inputs, num_hiddens, num_outputs, **kwargs):\n",
|
||
" super(Aggregate, self).__init__(**kwargs)\n",
|
||
" self.h = mlp(num_inputs, num_hiddens, flatten=True)\n",
|
||
" self.linear = nn.Linear(num_hiddens, num_outputs)\n",
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"\n",
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||
" def forward(self, V_A, V_B):\n",
|
||
" # 对两组比较向量分别求和\n",
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" V_A = V_A.sum(dim=1)\n",
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||
" V_B = V_B.sum(dim=1)\n",
|
||
" # 将两个求和结果的连结送到多层感知机中\n",
|
||
" Y_hat = self.linear(self.h(torch.cat([V_A, V_B], dim=1)))\n",
|
||
" return Y_hat"
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||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "9f9b14fe",
|
||
"metadata": {
|
||
"origin_pos": 20
|
||
},
|
||
"source": [
|
||
"### 整合代码\n",
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||
"\n",
|
||
"通过将注意步骤、比较步骤和聚合步骤组合在一起，我们定义了可分解注意力模型来联合训练这三个步骤。\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "6b99cb6a",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2023-08-18T06:58:29.583787Z",
|
||
"iopub.status.busy": "2023-08-18T06:58:29.583094Z",
|
||
"iopub.status.idle": "2023-08-18T06:58:29.593524Z",
|
||
"shell.execute_reply": "2023-08-18T06:58:29.592256Z"
|
||
},
|
||
"origin_pos": 22,
|
||
"tab": [
|
||
"pytorch"
|
||
]
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"class DecomposableAttention(nn.Module):\n",
|
||
" def __init__(self, vocab, embed_size, num_hiddens, num_inputs_attend=100,\n",
|
||
" num_inputs_compare=200, num_inputs_agg=400, **kwargs):\n",
|
||
" super(DecomposableAttention, self).__init__(**kwargs)\n",
|
||
" self.embedding = nn.Embedding(len(vocab), embed_size)\n",
|
||
" self.attend = Attend(num_inputs_attend, num_hiddens)\n",
|
||
" self.compare = Compare(num_inputs_compare, num_hiddens)\n",
|
||
" # 有3种可能的输出：蕴涵、矛盾和中性\n",
|
||
" self.aggregate = Aggregate(num_inputs_agg, num_hiddens, num_outputs=3)\n",
|
||
"\n",
|
||
" def forward(self, X):\n",
|
||
" premises, hypotheses = X\n",
|
||
" A = self.embedding(premises)\n",
|
||
" B = self.embedding(hypotheses)\n",
|
||
" beta, alpha = self.attend(A, B)\n",
|
||
" V_A, V_B = self.compare(A, B, beta, alpha)\n",
|
||
" Y_hat = self.aggregate(V_A, V_B)\n",
|
||
" return Y_hat"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e6989df0",
|
||
"metadata": {
|
||
"origin_pos": 24
|
||
},
|
||
"source": [
|
||
"## 训练和评估模型\n",
|
||
"\n",
|
||
"现在，我们将在SNLI数据集上对定义好的可分解注意力模型进行训练和评估。我们从读取数据集开始。\n",
|
||
"\n",
|
||
"### 读取数据集\n",
|
||
"\n",
|
||
"我们使用 :numref:`sec_natural-language-inference-and-dataset`中定义的函数下载并读取SNLI数据集。批量大小和序列长度分别设置为$256$和$50$。\n"
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]
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"origin_pos": 25,
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"tab": [
|
||
"pytorch"
|
||
]
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Downloading ../data/snli_1.0.zip from https://nlp.stanford.edu/projects/snli/snli_1.0.zip...\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"read 549367 examples\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"read 9824 examples\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"batch_size, num_steps = 256, 50\n",
|
||
"train_iter, test_iter, vocab = d2l.load_data_snli(batch_size, num_steps)"
|
||
]
|
||
},
|
||
{
|
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"cell_type": "markdown",
|
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"id": "effd33ee",
|
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"metadata": {
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"origin_pos": 27
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},
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"source": [
|
||
"### 创建模型\n",
|
||
"\n",
|
||
"我们使用预训练好的100维GloVe嵌入来表示输入词元。我们将向量$\\mathbf{a}_i$和$\\mathbf{b}_j$在 :eqref:`eq_nli_e`中的维数预定义为100。 :eqref:`eq_nli_e`中的函数$f$和 :eqref:`eq_nli_v_ab`中的函数$g$的输出维度被设置为200.然后我们创建一个模型实例，初始化它的参数，并加载GloVe嵌入来初始化输入词元的向量。\n"
|
||
]
|
||
},
|
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{
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"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "5587af56",
|
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|
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|
||
"shell.execute_reply": "2023-08-18T06:59:44.670793Z"
|
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},
|
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"origin_pos": 29,
|
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"tab": [
|
||
"pytorch"
|
||
]
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"embed_size, num_hiddens, devices = 100, 200, d2l.try_all_gpus()\n",
|
||
"net = DecomposableAttention(vocab, embed_size, num_hiddens)\n",
|
||
"glove_embedding = d2l.TokenEmbedding('glove.6b.100d')\n",
|
||
"embeds = glove_embedding[vocab.idx_to_token]\n",
|
||
"net.embedding.weight.data.copy_(embeds);"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "bccfd50d",
|
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"metadata": {
|
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"origin_pos": 31
|
||
},
|
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"source": [
|
||
"### 训练和评估模型\n",
|
||
"\n",
|
||
"与 :numref:`sec_multi_gpu`中接受单一输入（如文本序列或图像）的`split_batch`函数不同，我们定义了一个`split_batch_multi_inputs`函数以小批量接受多个输入，如前提和假设。\n"
|
||
]
|
||
},
|
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{
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"cell_type": "markdown",
|
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"id": "526be438",
|
||
"metadata": {
|
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"origin_pos": 33
|
||
},
|
||
"source": [
|
||
"现在我们可以在SNLI数据集上训练和评估模型。\n"
|
||
]
|
||
},
|
||
{
|
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"cell_type": "code",
|
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|
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"id": "ad101b85",
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"origin_pos": 35,
|
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"tab": [
|
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"pytorch"
|
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]
|
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},
|
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"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
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"loss 0.497, train acc 0.805, test acc 0.824\n",
|
||
"14163.6 examples/sec on [device(type='cuda', index=0), device(type='cuda', index=1)]\n"
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"lr, num_epochs = 0.001, 4\n",
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||
"trainer = torch.optim.Adam(net.parameters(), lr=lr)\n",
|
||
"loss = nn.CrossEntropyLoss(reduction=\"none\")\n",
|
||
"d2l.train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs,\n",
|
||
" devices)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d6855e87",
|
||
"metadata": {
|
||
"origin_pos": 37
|
||
},
|
||
"source": [
|
||
"### 使用模型\n",
|
||
"\n",
|
||
"最后，定义预测函数，输出一对前提和假设之间的逻辑关系。\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "4bea7172",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2023-08-18T07:02:56.422878Z",
|
||
"iopub.status.busy": "2023-08-18T07:02:56.422048Z",
|
||
"iopub.status.idle": "2023-08-18T07:02:56.431275Z",
|
||
"shell.execute_reply": "2023-08-18T07:02:56.430126Z"
|
||
},
|
||
"origin_pos": 39,
|
||
"tab": [
|
||
"pytorch"
|
||
]
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"#@save\n",
|
||
"def predict_snli(net, vocab, premise, hypothesis):\n",
|
||
" \"\"\"预测前提和假设之间的逻辑关系\"\"\"\n",
|
||
" net.eval()\n",
|
||
" premise = torch.tensor(vocab[premise], device=d2l.try_gpu())\n",
|
||
" hypothesis = torch.tensor(vocab[hypothesis], device=d2l.try_gpu())\n",
|
||
" label = torch.argmax(net([premise.reshape((1, -1)),\n",
|
||
" hypothesis.reshape((1, -1))]), dim=1)\n",
|
||
" return 'entailment' if label == 0 else 'contradiction' if label == 1 \\\n",
|
||
" else 'neutral'"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e586a5a5",
|
||
"metadata": {
|
||
"origin_pos": 41
|
||
},
|
||
"source": [
|
||
"我们可以使用训练好的模型来获得对示例句子的自然语言推断结果。\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "9830eae7",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2023-08-18T07:02:56.436285Z",
|
||
"iopub.status.busy": "2023-08-18T07:02:56.435278Z",
|
||
"iopub.status.idle": "2023-08-18T07:02:56.447228Z",
|
||
"shell.execute_reply": "2023-08-18T07:02:56.446040Z"
|
||
},
|
||
"origin_pos": 42,
|
||
"tab": [
|
||
"pytorch"
|
||
]
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"'contradiction'"
|
||
]
|
||
},
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"predict_snli(net, vocab, ['he', 'is', 'good', '.'], ['he', 'is', 'bad', '.'])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e5ddb189",
|
||
"metadata": {
|
||
"origin_pos": 43
|
||
},
|
||
"source": [
|
||
"## 小结\n",
|
||
"\n",
|
||
"* 可分解注意模型包括三个步骤来预测前提和假设之间的逻辑关系：注意、比较和聚合。\n",
|
||
"* 通过注意力机制，我们可以将一个文本序列中的词元与另一个文本序列中的每个词元对齐，反之亦然。这种对齐是使用加权平均的软对齐，其中理想情况下较大的权重与要对齐的词元相关联。\n",
|
||
"* 在计算注意力权重时，分解技巧会带来比二次复杂度更理想的线性复杂度。\n",
|
||
"* 我们可以使用预训练好的词向量作为下游自然语言处理任务（如自然语言推断）的输入表示。\n",
|
||
"\n",
|
||
"## 练习\n",
|
||
"\n",
|
||
"1. 使用其他超参数组合训练模型，能在测试集上获得更高的准确度吗？\n",
|
||
"1. 自然语言推断的可分解注意模型的主要缺点是什么？\n",
|
||
"1. 假设我们想要获得任何一对句子的语义相似级别（例如，0～1之间的连续值）。我们应该如何收集和标注数据集？请尝试设计一个有注意力机制的模型。\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e98a7edc",
|
||
"metadata": {
|
||
"origin_pos": 45,
|
||
"tab": [
|
||
"pytorch"
|
||
]
|
||
},
|
||
"source": [
|
||
"[Discussions](https://discuss.d2l.ai/t/5728)\n"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"language_info": {
|
||
"name": "python"
|
||
},
|
||
"required_libs": []
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
} |