Refine Chinese translation

Author: pengchzn

translate_cache/transformers/feed_forward.zh.json

@@ -1,17 +1,17 @@
 {
- "<h1>Position-wise Feed-Forward Network (FFN)</h1>\n<p>This is a <a href=\"https://pytorch.org\">PyTorch</a> implementation of position-wise feedforward network used in transformer.</p>\n<p>FFN consists of two fully connected layers. Number of dimensions in the hidden layer <span translate=no>_^_0_^_</span>, is generally set to around four times that of the token embedding <span translate=no>_^_1_^_</span>. So it is sometime also called the expand-and-contract network.</p>\n<p>There is an activation at the hidden layer, which is usually set to ReLU (Rectified Linear Unit) activation, <span translate=no>_^_2_^_</span></p>\n<p>That is, the FFN function is, <span translate=no>_^_3_^_</span> where <span translate=no>_^_4_^_</span>, <span translate=no>_^_5_^_</span>, <span translate=no>_^_6_^_</span> and <span translate=no>_^_7_^_</span> are learnable parameters.</p>\n<p>Sometimes the GELU (Gaussian Error Linear Unit) activation is also used instead of ReLU. <span translate=no>_^_8_^_</span> where <span translate=no>_^_9_^_</span></p>\n<h3>Gated Linear Units</h3>\n<p>This is a generic implementation that supports different variants including <a href=\"https://arxiv.org/abs/2002.05202\">Gated Linear Units</a> (GLU). We have also implemented experiments on these:</p>\n<ul><li><a href=\"glu_variants/experiment.html\">experiment that uses <span translate=no>_^_10_^_</span></a> </li>\n<li><a href=\"glu_variants/simple.html\">simpler version from scratch</a></li></ul>\n": "<h1>\u4f4d\u7f6e\u524d\u9988\u7f51\u7edc (FFN)</h1>\n<p>\u8fd9\u662f\u53d8\u538b\u5668\u4e2d\u4f7f\u7528\u7684\u6309\u4f4d\u7f6e\u524d\u9988\u7f51\u7edc\u7684 <a href=\"https://pytorch.org\">PyTorch</a> \u5b9e\u73b0\u3002</p>\n<p>FFN \u7531\u4e24\u4e2a\u5b8c\u5168\u8fde\u63a5\u7684\u5c42\u7ec4\u6210\u3002\u9690\u85cf\u5c42\u4e2d\u7684\u7ef4\u5ea6\u6570<span translate=no>_^_0_^_</span>\uff0c\u901a\u5e38\u8bbe\u7f6e\u4e3a\u4ee4\u724c\u5d4c\u5165\u7684\u56db\u500d\u5de6\u53f3<span translate=no>_^_1_^_</span>\u3002\u56e0\u6b64\uff0c\u5b83\u6709\u65f6\u4e5f\u88ab\u79f0\u4e3a\u6269\u5f20\u548c\u6536\u7f29\u7f51\u7edc\u3002</p>\n<p>\u9690\u85cf\u5c42\u6709\u4e00\u4e2a\u6fc0\u6d3b\uff0c\u901a\u5e38\u8bbe\u7f6e\u4e3aRelU\uff08\u6574\u6d41\u7ebf\u6027\u5355\u5143\uff09\u6fc0\u6d3b\uff0c<span translate=no>_^_2_^_</span></p>\n<p>\u4e5f\u5c31\u662f\u8bf4\uff0cFFN \u51fd\u6570\u662f\u3001<span translate=no>_^_3_^_</span>\u5176\u4e2d<span translate=no>_^_4_^_</span><span translate=no>_^_5_^_</span>\u3001<span translate=no>_^_6_^_</span>\u548c<span translate=no>_^_7_^_</span>\u662f\u53ef\u5b66\u4e60\u7684\u53c2\u6570\u3002</p>\n<p>\u6709\u65f6\u8fd8\u4f1a\u4f7f\u7528 GELU\uff08\u9ad8\u65af\u8bef\u5dee\u7ebf\u6027\u5355\u4f4d\uff09\u6fc0\u6d3b\u6765\u4ee3\u66ff RelU\u3002<span translate=no>_^_8_^_</span>\u5728\u54ea\u91cc<span translate=no>_^_9_^_</span></p>\n<h3>\u95e8\u63a7\u7ebf\u6027\u5355\u5143</h3>\n<p>\u8fd9\u662f\u4e00\u4e2a\u901a\u7528\u5b9e\u73b0\uff0c\u652f\u6301\u4e0d\u540c\u7684\u53d8\u4f53\uff0c\u5305\u62ec<a href=\"https://arxiv.org/abs/2002.05202\">\u95e8\u63a7\u7ebf\u6027\u5355\u5143</a> (GLU)\u3002\u6211\u4eec\u8fd8\u5bf9\u4ee5\u4e0b\u65b9\u9762\u8fdb\u884c\u4e86\u5b9e\u9a8c\uff1a</p>\n<ul><li><a href=\"glu_variants/experiment.html\">\u4f7f\u7528\u7684\u5b9e\u9a8c<span translate=no>_^_10_^_</span></a></li>\n<li><a href=\"glu_variants/simple.html\">\u4ece\u5934\u5f00\u59cb\u66f4\u7b80\u5355\u7684\u7248\u672c</a></li></ul>\n",
+ "<h1>Position-wise Feed-Forward Network (FFN)</h1>\n<p>This is a <a href=\"https://pytorch.org\">PyTorch</a> implementation of position-wise feedforward network used in transformer.</p>\n<p>FFN consists of two fully connected layers. Number of dimensions in the hidden layer <span translate=no>_^_0_^_</span>, is generally set to around four times that of the token embedding <span translate=no>_^_1_^_</span>. So it is sometime also called the expand-and-contract network.</p>\n<p>There is an activation at the hidden layer, which is usually set to ReLU (Rectified Linear Unit) activation, <span translate=no>_^_2_^_</span></p>\n<p>That is, the FFN function is, <span translate=no>_^_3_^_</span> where <span translate=no>_^_4_^_</span>, <span translate=no>_^_5_^_</span>, <span translate=no>_^_6_^_</span> and <span translate=no>_^_7_^_</span> are learnable parameters.</p>\n<p>Sometimes the GELU (Gaussian Error Linear Unit) activation is also used instead of ReLU. <span translate=no>_^_8_^_</span> where <span translate=no>_^_9_^_</span></p>\n<h3>Gated Linear Units</h3>\n<p>This is a generic implementation that supports different variants including <a href=\"https://arxiv.org/abs/2002.05202\">Gated Linear Units</a> (GLU). We have also implemented experiments on these:</p>\n<ul><li><a href=\"glu_variants/experiment.html\">experiment that uses <span translate=no>_^_10_^_</span></a> </li>\n<li><a href=\"glu_variants/simple.html\">simpler version from scratch</a></li></ul>\n": "<h1>\u4f4d\u7f6e\u524d\u9988\u7f51\u7edc （FFN）</h1>\n<p>\u8fd9\u662f Transformer \u4e2d\u4f7f\u7528\u7684\u4f4d\u7f6e\u524d\u9988\u7f51\u7edc\u7684 <a href=\"https://pytorch.org\"> PyTorch </a> \u5b9e\u73b0\u3002</p>\n<p> FFN \u7531\u4e24\u4e2a\u5168\u8fde\u63a5\u5c42\u7ec4\u6210\u3002\u9690\u85cf\u5c42\u4e2d\u7684\u7ef4\u5ea6\u6570<span translate=no>_%5e_0_%5e_</span>\u901a\u5e38\u8bbe\u7f6e\u4e3a\u6807\u8bb0\u5d4c\u5165\u7ef4\u5ea6<span translate=no>_%5e_1_%5e_</span>\u7684\u56db\u500d\u5de6\u53f3\u3002\u56e0\u6b64\uff0c\u5b83\u6709\u65f6\u4e5f\u88ab\u79f0\u4e3a\u6269\u5f20-\u538b\u7f29\u7f51\u7edc\u3002</p>\n<p>\u9690\u85cf\u5c42\u6709\u4e00\u4e2a\u6fc0\u6d3b\u51fd\u6570\uff0c\u901a\u5e38\u8bbe\u7f6e\u4e3a ReLU (Rectified Linear Unit) \u6fc0\u6d3b\u51fd\u6570\uff0c<span translate=no>_%5e_2_%5e_</span></p>\n<p>\u5728\u6b64\u57fa\u7840\u4e0a\uff0c FFN \u51fd\u6570\u53ef\u4ee5\u5199\u4f5c\uff1a<span translate=no>_%5e_3_%5e_</span>\u5176\u4e2d<span translate=no>_%5e_4_%5e_</span><span translate=no>_%5e_5_%5e_</span>\u3001<span translate=no>_%5e_6_%5e_</span>\u548c<span translate=no>_%5e_7_%5e_</span>\u662f\u53ef\u5b66\u4e60\u7684\u53c2\u6570\u3002</p>\n<p>\u6709\u65f6\u8fd8\u4f1a\u4f7f\u7528 GELU (Gaussian Error Linear Unit) \u6fc0\u6d3b\u51fd\u6570\u6765\u4ee3\u66ff ReLU \u3002<span translate=no>_%5e_8_%5e_</span>\u5176\u4e2d<span translate=no>_%5e_9_%5e_</span></p>\n<h3>\u95e8\u63a7\u7ebf\u6027\u5355\u5143</h3>\n<p>\u8fd9\u662f\u4e00\u4e2a\u901a\u7528\u5b9e\u73b0\uff0c\u652f\u6301\u5305\u62ec<a href=\"https://arxiv.org/abs/2002.05202\">\u95e8\u63a7\u7ebf\u6027\u5355\u5143(GLU)</a> \u5728\u5185\u7684\u4e0d\u540c\u53d8\u4f53\u3002\u6211\u4eec\u8fd8\u5bf9\u8fd9\u4e9b\u8fdb\u884c\u4e86\u5b9e\u9a8c\uff1a</p>\n<ul><li><a href=\"glu_variants/experiment.html\">\u4f7f\u7528<span translate=no>_%5e_10_%5e_</span></a>\u7684\u5b9e\u9a8c</li>\n<li><a href=\"glu_variants/simple.html\">\u4ece\u5934\u5f00\u59cb\u7684\u7b80\u5316\u7248\u672c</a></li></ul>\n",
  "<h2>FFN module</h2>\n": "<h2>FFN \u6a21\u5757</h2>\n",
  "<p><span translate=no>_^_0_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span></p>\n",
- "<p><span translate=no>_^_0_^_</span> or <span translate=no>_^_1_^_</span> depending on whether it is gated </p>\n": "<p><span translate=no>_^_0_^_</span>\u6216\u8005<span translate=no>_^_1_^_</span>\u53d6\u51b3\u4e8e\u5b83\u662f\u5426\u6709\u95e8\u63a7</p>\n",
- "<p>Activation function <span translate=no>_^_0_^_</span> </p>\n": "<p>\u6fc0\u6d3b\u529f\u80fd<span translate=no>_^_0_^_</span></p>\n",
- "<p>Apply dropout </p>\n": "<p>\u7533\u8bf7\u9000\u5b66</p>\n",
- "<p>Hidden layer dropout </p>\n": "<p>\u9690\u85cf\u56fe\u5c42\u4e22\u5931</p>\n",
- "<p>If gated, <span translate=no>_^_0_^_</span> </p>\n": "<p>\u5982\u679c\u662f\u5c01\u95ed\u7684\uff0c<span translate=no>_^_0_^_</span></p>\n",
- "<p>If there is a gate the linear layer to transform inputs to be multiplied by the gate, parameterized by weight <span translate=no>_^_0_^_</span> and bias <span translate=no>_^_1_^_</span> </p>\n": "<p>\u5982\u679c\u6709\u95e8\uff0c\u5219\u8f6c\u6362\u8f93\u5165\u7684\u7ebf\u6027\u5c42\u5c06\u4e58\u4ee5\u95e8\uff0c\u5e76\u901a\u8fc7\u6743\u91cd<span translate=no>_^_0_^_</span>\u548c\u504f\u7f6e\u8fdb\u884c\u53c2\u6570\u5316<span translate=no>_^_1_^_</span></p>\n",
- "<p>Layer one parameterized by weight <span translate=no>_^_0_^_</span> and bias <span translate=no>_^_1_^_</span> </p>\n": "<p>\u7b2c\u4e00\u5c42\u6309\u6743\u91cd<span translate=no>_^_0_^_</span>\u548c\u504f\u5dee\u8fdb\u884c\u53c2\u6570\u5316<span translate=no>_^_1_^_</span></p>\n",
+ "<p><span translate=no>_^_0_^_</span> or <span translate=no>_^_1_^_</span> depending on whether it is gated </p>\n": "<p>\u6839\u636e\u662f\u5426\u8fdb\u884c\u95e8\u63a7\uff0c\u8fd4\u56de<span translate=no>_^_0_^_</span>\u6216\u8005<span translate=no>_^_1_^_</span></p>\n",
+ "<p>Activation function <span translate=no>_^_0_^_</span> </p>\n": "<p>\u6fc0\u6d3b\u51fd\u6570<span translate=no>_^_0_^_</span></p>\n",
+ "<p>Apply dropout </p>\n": "<p>\u4f7f\u7528 Dropout</p>\n",
+ "<p>Hidden layer dropout </p>\n": "<p>\u9690\u85cf\u5c42 Dropout</p>\n",
+ "<p>If gated, <span translate=no>_^_0_^_</span> </p>\n": "<p>\u5982\u679c\u8fdb\u884c\u95e8\u63a7\uff0c<span translate=no>_^_0_^_</span></p>\n",
+ "<p>If there is a gate the linear layer to transform inputs to be multiplied by the gate, parameterized by weight <span translate=no>_^_0_^_</span> and bias <span translate=no>_^_1_^_</span> </p>\n": "<p>\u5982\u679c\u5b58\u5728\u95e8\u63a7\uff0c\u5219\u901a\u8fc7\u7ebf\u6027\u5c42\u5c06\u8f93\u5165\u503c\u4e0e\u95e8\u76f8\u4e58\uff0c\u5e76\u7531\u6743\u91cd <span translate=no>_^_0_^_</span>\u548c\u504f\u7f6e<span translate=no>_^_1_^_</span>\u8fdb\u884c\u53c2\u6570\u5316</p>\n",
+ "<p>Layer one parameterized by weight <span translate=no>_^_0_^_</span> and bias <span translate=no>_^_1_^_</span> </p>\n": "<p>\u7b2c\u4e00\u5c42\u7531\u6743\u91cd<span translate=no>_^_0_^_</span>\u548c\u504f\u5dee<span translate=no>_^_1_^_</span>\u8fdb\u884c\u53c2\u6570\u5316</p>\n",
  "<p>Otherwise </p>\n": "<p>\u5426\u5219</p>\n",
- "<p>Whether there is a gate </p>\n": "<p>\u662f\u5426\u6709\u95e8</p>\n",
- "<ul><li><span translate=no>_^_0_^_</span> is the number of features in a token embedding </li>\n<li><span translate=no>_^_1_^_</span> is the number of features in the hidden layer of the FFN </li>\n<li><span translate=no>_^_2_^_</span> is dropout probability for the hidden layer </li>\n<li><span translate=no>_^_3_^_</span> specifies whether the hidden layer is gated </li>\n<li><span translate=no>_^_4_^_</span> specified whether the first fully connected layer should have a learnable bias </li>\n<li><span translate=no>_^_5_^_</span> specified whether the second fully connected layer should have a learnable bias </li>\n<li><span translate=no>_^_6_^_</span> specified whether the fully connected layer for the gate should have a learnable bias</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u4ee4\u724c\u5d4c\u5165\u4e2d\u7684\u8981\u7d20\u6570\u91cf</li>\n<li><span translate=no>_^_1_^_</span>\u662f FFN \u9690\u85cf\u5c42\u4e2d\u7684\u8981\u7d20\u6570\u91cf</li>\n<li><span translate=no>_^_2_^_</span>\u662f\u9690\u85cf\u5c42\u7684\u4e22\u5931\u6982\u7387</li>\n<li><span translate=no>_^_3_^_</span>\u6307\u5b9a\u9690\u85cf\u5c42\u662f\u5426\u4e3a\u95e8\u63a7</li>\n<li><span translate=no>_^_4_^_</span>\u6307\u5b9a\u7b2c\u4e00\u4e2a\u5b8c\u5168\u8fde\u63a5\u7684\u5c42\u662f\u5426\u5e94\u8be5\u6709\u53ef\u5b66\u4e60\u7684\u504f\u5dee</li>\n<li><span translate=no>_^_5_^_</span>\u6307\u5b9a\u7b2c\u4e8c\u4e2a\u5b8c\u5168\u8fde\u63a5\u7684\u5c42\u662f\u5426\u5e94\u8be5\u6709\u53ef\u5b66\u4e60\u7684\u504f\u5dee</li>\n<li><span translate=no>_^_6_^_</span>\u6307\u5b9a\u95e8\u7684\u5168\u8fde\u63a5\u5c42\u662f\u5426\u5e94\u5177\u6709\u53ef\u5b66\u4e60\u7684\u504f\u5dee</li></ul>\n",
- "Documented reusable implementation of the position wise feedforward network.": "\u8bb0\u5f55\u4e86\u4f4d\u7f6e\u524d\u9988\u7f51\u7edc\u7684\u53ef\u91cd\u7528\u5b9e\u73b0\u3002",
+ "<p>Whether there is a gate </p>\n": "<p>\u662f\u5426\u5b58\u5728\u95e8\u63a7</p>\n",
+ "<ul><li><span translate=no>_^_0_^_</span> is the number of features in a token embedding </li>\n<li><span translate=no>_^_1_^_</span> is the number of features in the hidden layer of the FFN </li>\n<li><span translate=no>_^_2_^_</span> is dropout probability for the hidden layer </li>\n<li><span translate=no>_^_3_^_</span> specifies whether the hidden layer is gated </li>\n<li><span translate=no>_^_4_^_</span> specified whether the first fully connected layer should have a learnable bias </li>\n<li><span translate=no>_^_5_^_</span> specified whether the second fully connected layer should have a learnable bias </li>\n<li><span translate=no>_^_6_^_</span> specified whether the fully connected layer for the gate should have a learnable bias</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u6807\u8bb0\u5d4c\u5165\u4e2d\u7684\u7279\u5f81\u6570\u91cf</li>\n<li><span translate=no>_^_1_^_</span>\u662f FFN \u9690\u85cf\u5c42\u4e2d\u7684\u7279\u5f81\u6570\u91cf</li>\n<li><span translate=no>_^_2_^_</span>\u662f\u9690\u85cf\u5c42\u7684 Dropout \u7387</li>\n<li><span translate=no>_^_3_^_</span>\u6307\u5b9a\u4e86\u9690\u85cf\u5c42\u662f\u5426\u4e3a\u95e8\u63a7\u5c42</li>\n<li><span translate=no>_^_4_^_</span>\u6307\u5b9a\u4e86\u7b2c\u4e00\u4e2a\u5168\u8fde\u63a5\u5c42\u662f\u5426\u5e94\u8be5\u5177\u6709\u53ef\u5b66\u4e60\u7684\u504f\u7f6e</li>\n<li><span translate=no>_^_5_^_</span>\u6307\u5b9a\u7b2c\u4e8c\u4e2a\u5168\u8fde\u63a5\u5c42\u662f\u5426\u5e94\u5177\u6709\u53ef\u5b66\u4e60\u7684\u504f\u7f6e</li>\n<li><span translate=no>_^_6_^_</span>\u6307\u5b9a\u95e8\u63a7\u7684\u5168\u8fde\u63a5\u5c42\u662f\u5426\u5e94\u5177\u6709\u53ef\u5b66\u4e60\u7684\u504f\u7f6e</li></ul>\n",
+ "Documented reusable implementation of the position wise feedforward network.": "\u5df2\u8bb0\u5f55\u5e76\u53ef\u91cd\u590d\u4f7f\u7528\u7684\u4f4d\u7f6e\u524d\u9988\u7f51\u7edc\u5b9e\u73b0\u3002",
  "Position-wise Feed-Forward Network (FFN)": "\u4f4d\u7f6e\u524d\u9988\u7f51\u7edc (FFN)"
 }

translate_cache/transformers/label_smoothing_loss.zh.json

@@ -1,7 +1,7 @@
 {
  "<h1>Label Smoothing Loss</h1>\n": "<h1>\u6807\u7b7e\u5e73\u6ed1\u635f\u5931</h1>\n",
- "<p>Show the target distributions expected by the system. </p>\n": "<p>\u663e\u793a\u7cfb\u7edf\u9884\u671f\u7684\u76ee\u6807\u5206\u5e03\u3002</p>\n",
- "<p>print(predict) </p>\n": "<p>\u6253\u5370\uff08\u9884\u6d4b\uff09</p>\n",
+ "<p>Show the target distributions expected by the system. </p>\n": "<p>\u5c55\u793a\u7cfb\u7edf\u671f\u671b\u7684\u76ee\u6807\u5206\u5e03\u3002</p>\n",
+ "<p>print(predict) </p>\n": "<p>\u8f93\u51fa\uff08\u9884\u6d4b\uff09</p>\n",
  "Label Smoothing Loss": "\u6807\u7b7e\u5e73\u6ed1\u635f\u5931",
- "This is an implementation of label smoothing loss, that can be used as an alternative to cross entropy loss for improved accuracy.": "\u8fd9\u662f\u6807\u7b7e\u5e73\u6ed1\u635f\u5931\u7684\u5b9e\u73b0\uff0c\u53ef\u4ee5\u7528\u4f5c\u4ea4\u53c9\u71b5\u635f\u5931\u7684\u66ff\u4ee3\u65b9\u6848\uff0c\u4ee5\u63d0\u9ad8\u51c6\u786e\u6027\u3002"
+ "This is an implementation of label smoothing loss, that can be used as an alternative to cross entropy loss for improved accuracy.": "\u8fd9\u662f\u6807\u7b7e\u5e73\u6ed1\u635f\u5931\u7684\u5b9e\u73b0\uff0c\u53ef\u4f5c\u4e3a\u4ea4\u53c9\u71b5\u635f\u5931\u7684\u66ff\u4ee3\u54c1\u4ee5\u63d0\u9ad8\u51c6\u786e\u6027\u3002"
 }