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translate_cache/transformers/aft/__init__.ja.json
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translate_cache/transformers/aft/__init__.si.json
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translate_cache/transformers/aft/__init__.zh.json
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translate_cache/transformers/aft/experiment.ja.json
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{
"<h1><a href=\"index.html\">Attention Free Transformer (AFT)</a> Experiment</h1>\n<p>This is an annotated PyTorch experiment to train a <a href=\"index.html\">AFT model</a>.</p>\n<p>This is based on <a href=\"../../experiments/nlp_autoregression.html\">general training loop and configurations for auto-regressive NLP task</a>.</p>\n": "<h1><a href=\"index.html\">\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30d5\u30ea\u30fc\u5909\u5727\u5668\uff08AFT\uff09\u5b9f\u9a13</a></h1>\n<p><a href=\"index.html\">\u3053\u308c\u306f\u3001AFT\u30e2\u30c7\u30eb\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3059\u308b\u305f\u3081\u306e\u6ce8\u91c8\u4ed8\u304d\u306ePyTorch\u5b9f\u9a13\u3067\u3059\u3002</a></p>\n<p>\u3053\u308c\u306f\u3001<a href=\"../../experiments/nlp_autoregression.html\">\u4e00\u822c\u7684\u306a\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30eb\u30fc\u30d7\u3068\u81ea\u5df1\u56de\u5e30\u578bNLP\u30bf\u30b9\u30af\u306e\u8a2d\u5b9a\u306b\u57fa\u3065\u3044\u3066\u3044\u307e\u3059</a>\u3002</p>\n",
"<h2>Configurations</h2>\n<p>This inherits from <a href=\"../../experiments/nlp_autoregression.html#NLPAutoRegressionConfigs\"><span translate=no>_^_0_^_</span></a></p>\n": "<h2>\u30b3\u30f3\u30d5\u30a3\u30ae\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3</h2>\n<p>\u3053\u308c\u306f\u4ee5\u4e0b\u304b\u3089\u7d99\u627f\u3055\u308c\u307e\u3059 <a href=\"../../experiments/nlp_autoregression.html#NLPAutoRegressionConfigs\"><span translate=no>_^_0_^_</span></a></p>\n",
"<h2>Simple autoregressive model</h2>\n<p>This consists of a token embedding layer, transformer encoder, and a final linear layer that gives token logits.</p>\n": "<h2>\u5358\u7d14\u306a\u81ea\u5df1\u56de\u5e30\u30e2\u30c7\u30eb</h2>\n<p>\u3053\u308c\u306f\u3001\u30c8\u30fc\u30af\u30f3\u57cb\u3081\u8fbc\u307f\u5c64\u3001\u30c8\u30e9\u30f3\u30b9\u30d5\u30a9\u30fc\u30de\u30fc\u30a8\u30f3\u30b3\u30fc\u30c0\u30fc\u3001\u304a\u3088\u3073\u30c8\u30fc\u30af\u30f3\u30ed\u30b8\u30c3\u30c8\u3092\u63d0\u4f9b\u3059\u308b\u6700\u5f8c\u306e\u7dda\u5f62\u5c64\u3067\u69cb\u6210\u3055\u308c\u3066\u3044\u307e\u3059\u3002</p>\n",
"<h3>Transformer configurations</h3>\n": "<h3>\u5909\u5727\u5668\u69cb\u6210</h3>\n",
"<p> </p>\n": "<p></p>\n",
"<p> Create an auto-regressive model</p>\n": "<p>\u81ea\u5df1\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u4f5c\u6210</p>\n",
"<p>Batch size <span translate=no>_^_0_^_</span> </p>\n": "<p>\u30d0\u30c3\u30c1\u30b5\u30a4\u30ba <span translate=no>_^_0_^_</span></p>\n",
"<p>Create configs </p>\n": "<p>\u30b3\u30f3\u30d5\u30a3\u30b0\u306e\u4f5c\u6210</p>\n",
"<p>Create experiment </p>\n": "<p>\u5b9f\u9a13\u3092\u4f5c\u6210</p>\n",
"<p>Create subsequent mask if mask is not initialized or if the size of the mask is different </p>\n": "<p>\u30de\u30b9\u30af\u304c\u521d\u671f\u5316\u3055\u308c\u3066\u3044\u306a\u3044\u5834\u5408\u3084\u30de\u30b9\u30af\u306e\u30b5\u30a4\u30ba\u304c\u7570\u306a\u308b\u5834\u5408\u306f\u3001\u5f8c\u7d9a\u306e\u30de\u30b9\u30af\u3092\u4f5c\u6210\u3057\u307e\u3059</p>\n",
"<p>Embedding size </p>\n": "<p>\u57cb\u3081\u8fbc\u307f\u30b5\u30a4\u30ba</p>\n",
"<p>FFN hidden dimension size </p>\n": "<p>FFN \u96a0\u3057\u30c7\u30a3\u30e1\u30f3\u30b7\u30e7\u30f3\u30b5\u30a4\u30ba</p>\n",
"<p>GPT model </p>\n": "<p>GPT \u30e2\u30c7\u30eb</p>\n",
"<p>Get logits </p>\n": "<p>\u30ed\u30b8\u30c3\u30c8\u3092\u53d6\u5f97</p>\n",
"<p>Get the token embeddings with positional encodings </p>\n": "<p>\u4f4d\u7f6e\u30a8\u30f3\u30b3\u30fc\u30c7\u30a3\u30f3\u30b0\u306b\u3088\u308b\u30c8\u30fc\u30af\u30f3\u306e\u57cb\u3081\u8fbc\u307f\u3092\u53d6\u5f97</p>\n",
"<p>Optimizer </p>\n": "<p>\u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u30fc</p>\n",
"<p>Override configurations </p>\n": "<p>\u30aa\u30fc\u30d0\u30fc\u30e9\u30a4\u30c9\u8a2d\u5b9a</p>\n",
"<p>Prompt separator is blank </p>\n": "<p>\u30d7\u30ed\u30f3\u30d7\u30c8\u30bb\u30d1\u30ec\u30fc\u30bf\u304c\u7a7a\u767d</p>\n",
"<p>Replace self-attention with an <a href=\"index.html\">AFT Local Module</a> </p>\n": "<p><a href=\"index.html\">\u30bb\u30eb\u30d5\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u3092AFT\u30ed\u30fc\u30ab\u30eb\u30e2\u30b8\u30e5\u30fc\u30eb\u306b\u7f6e\u304d\u63db\u3048\u308b</a></p>\n",
"<p>Return results (second value is for state, since our trainer is used with RNNs also) </p>\n": "<p>\u7d50\u679c\u3092\u8fd4\u3057\u307e\u3059\uff08\u30c8\u30ec\u30fc\u30ca\u30fc\u306fRNN\u3067\u3082\u4f7f\u7528\u3055\u308c\u308b\u305f\u3081\u30012\u756a\u76ee\u306e\u5024\u306f\u72b6\u614b\u7528\u3067\u3059\uff09</p>\n",
"<p>Run training </p>\n": "<p>\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3092\u5b9f\u884c</p>\n",
"<p>Set models for saving and loading </p>\n": "<p>\u4fdd\u5b58\u304a\u3088\u3073\u8aad\u307f\u8fbc\u307f\u7528\u306e\u30e2\u30c7\u30eb\u3092\u8a2d\u5b9a\u3059\u308b</p>\n",
"<p>Set the embedding size </p>\n": "<p>\u57cb\u3081\u8fbc\u307f\u30b5\u30a4\u30ba\u3092\u8a2d\u5b9a</p>\n",
"<p>Set the vocabulary sizes for embeddings and generating logits </p>\n": "<p>\u57cb\u3081\u8fbc\u307f\u3084\u30ed\u30b8\u30c3\u30c8\u306e\u751f\u6210\u306b\u4f7f\u7528\u3059\u308b\u30dc\u30ad\u30e3\u30d6\u30e9\u30ea\u30fc\u30b5\u30a4\u30ba\u3092\u8a2d\u5b9a</p>\n",
"<p>Start the experiment </p>\n": "<p>\u5b9f\u9a13\u3092\u59cb\u3081\u308b</p>\n",
"<p>Starting prompt for sampling </p>\n": "<p>\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u306e\u958b\u59cb\u30d7\u30ed\u30f3\u30d7\u30c8</p>\n",
"<p>Subsequent mask, will mask out tokens from seeing future tokens </p>\n": "<p>\u6b21\u306b\u30de\u30b9\u30af\u3059\u308b\u3068\u3001\u30c8\u30fc\u30af\u30f3\u304c\u30de\u30b9\u30af\u3055\u308c\u3001\u5c06\u6765\u306e\u30c8\u30fc\u30af\u30f3\u304c\u898b\u3048\u306a\u304f\u306a\u308a\u307e\u3059</p>\n",
"<p>Switch between training and validation for <span translate=no>_^_0_^_</span> times per epoch </p>\n": "<p>\u30a8\u30dd\u30c3\u30af\u3054\u3068\u306b\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3068\u691c\u8a3c\u3092\u5207\u308a\u66ff\u3048\u308b <span translate=no>_^_0_^_</span></p>\n",
"<p>The mask will be initialized on the first call </p>\n": "<p>\u30de\u30b9\u30af\u306f\u6700\u521d\u306e\u547c\u3073\u51fa\u3057\u3067\u521d\u671f\u5316\u3055\u308c\u307e\u3059</p>\n",
"<p>Train for <span translate=no>_^_0_^_</span> epochs </p>\n": "<p><span translate=no>_^_0_^_</span>\u6642\u4ee3\u306b\u5408\u308f\u305b\u305f\u5217\u8eca</p>\n",
"<p>Transformer </p>\n": "<p>\u5909\u5727\u5668</p>\n",
"<p>Transformer encoder </p>\n": "<p>\u30c8\u30e9\u30f3\u30b9\u30a8\u30f3\u30b3\u30fc\u30c0\u30fc</p>\n",
"<p>Use Tiny Shakespeare dataset </p>\n": "<p>\u30bf\u30a4\u30cb\u30fc\u30fb\u30b7\u30a7\u30a4\u30af\u30b9\u30d4\u30a2\u30fb\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u3046</p>\n",
"<p>Use a context size of <span translate=no>_^_0_^_</span> </p>\n": "<p>\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u30b5\u30a4\u30ba\u3092\u6b21\u306e\u5024\u306b\u3057\u3066\u304f\u3060\u3055\u3044 <span translate=no>_^_0_^_</span></p>\n",
"<p>Use character level tokenizer </p>\n": "<p>\u30ad\u30e3\u30e9\u30af\u30bf\u30fc\u30ec\u30d9\u30eb\u306e\u30c8\u30fc\u30af\u30ca\u30a4\u30b6\u30fc\u3092\u4f7f\u3046</p>\n",
"<p>We use our <a href=\"../configs.html#TransformerConfigs\">configurable transformer implementation</a> </p>\n": "<p><a href=\"../configs.html#TransformerConfigs\">\u8a2d\u5b9a\u53ef\u80fd\u306a\u30c8\u30e9\u30f3\u30b9\u5b9f\u88c5\u3092\u4f7f\u7528\u3057\u3066\u3044\u307e\u3059</a></p>\n",
"<ul><li><span translate=no>_^_0_^_</span> is the transformer <a href=\"../models.html#Encoder\">Encoder</a> </li>\n<li><span translate=no>_^_1_^_</span> is the token <a href=\"../models.html#EmbeddingsWithLearnedPositionalEncoding\">embedding module (with positional encodings)</a> </li>\n<li><span translate=no>_^_2_^_</span> is the <a href=\"../models.html#Generator\">final fully connected layer</a> that gives the logits.</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span><a href=\"../models.html#Encoder\">\u5909\u5727\u5668\u30a8\u30f3\u30b3\u30fc\u30c0\u3067\u3059</a></li>\n<li><span translate=no>_^_1_^_</span><a href=\"../models.html#EmbeddingsWithLearnedPositionalEncoding\">\u306f\u30c8\u30fc\u30af\u30f3\u57cb\u3081\u8fbc\u307f\u30e2\u30b8\u30e5\u30fc\u30eb\u3067\u3059 (\u4f4d\u7f6e\u30a8\u30f3\u30b3\u30fc\u30c7\u30a3\u30f3\u30b0\u4ed8\u304d)</a></li>\n</ul><li><span translate=no>_^_2_^_</span><a href=\"../models.html#Generator\">\u30ed\u30b8\u30c3\u30c8\u3092\u751f\u6210\u3059\u308b\u6700\u5f8c\u306e\u5b8c\u5168\u63a5\u7d9a\u5c64\u3067\u3059</a>\u3002</li>\n",
"Attention Free Transformer (AFT) Experiment": "\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30d5\u30ea\u30fc\u5909\u5727\u5668\uff08AFT\uff09\u5b9f\u9a13",
"This experiment trains an Attention Free Transformer (AFT) based model on Tiny Shakespeare dataset.": "\u3053\u306e\u5b9f\u9a13\u3067\u306f\u3001\u30bf\u30a4\u30cb\u30fc\u30fb\u30b7\u30a7\u30a4\u30af\u30b9\u30d4\u30a2\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u57fa\u3065\u3044\u3066\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30fb\u30d5\u30ea\u30fc\u30fb\u30c8\u30e9\u30f3\u30b9\u30d5\u30a9\u30fc\u30de\u30fc\uff08AFT\uff09\u30d9\u30fc\u30b9\u306e\u30e2\u30c7\u30eb\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3057\u307e\u3059\u3002"
}
translate_cache/transformers/aft/experiment.si.json
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{
"<h1><a href=\"index.html\">Attention Free Transformer (AFT)</a> Experiment</h1>\n<p>This is an annotated PyTorch experiment to train a <a href=\"index.html\">AFT model</a>.</p>\n<p>This is based on <a href=\"../../experiments/nlp_autoregression.html\">general training loop and configurations for auto-regressive NLP task</a>.</p>\n<p><a href=\"https://app.labml.ai/run/6348e504c3a511eba9529daa283fb495\"><span translate=no>_^_0_^_</span></a></p>\n": "<h1><a href=\"index.html\">\u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0db1\u0dd2\u0daf\u0dc4\u0dc3\u0dca \u0da7\u0dca\u0dbb\u0dcf\u0db1\u0dca\u0dc3\u0dca\u0dc6\u0ddd\u0db8\u0dbb\u0dca (AFT)</a> \u0d85\u0dad\u0dca\u0dc4\u0daf\u0dcf \u0db6\u0dd0\u0dbd\u0dd3\u0db8</h1>\n<p>\u0db8\u0dd9\u0dba <a href=\"index.html\">AFT \u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba\u0d9a\u0dca</a>\u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4 \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0d9a\u0dbb\u0db1 \u0dbd\u0daf PyTorch \u0d85\u0dad\u0dca\u0dc4\u0daf\u0dcf \u0db6\u0dd0\u0dbd\u0dd3\u0db8\u0d9a\u0dd2. </p>\n<p>\u0db8\u0dd9\u0dba <a href=\"../../experiments/nlp_autoregression.html\">\u0dc3\u0dca\u0dc0\u0dba\u0d82\u0d9a\u0dca\u0dbb\u0dd3\u0dba \u0db4\u0dca\u0dbb\u0dad\u0dd2\u0d9c\u0dcf\u0db8\u0dd3 \u0d91\u0db1\u0dca\u0d91\u0dbd\u0dca\u0db4\u0dd3 \u0d9a\u0dcf\u0dbb\u0dca\u0dba\u0dba \u0dc3\u0db3\u0dc4\u0dcf \u0dc3\u0dcf\u0db8\u0dcf\u0db1\u0dca\u0dba \u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4 \u0dbd\u0dd6\u0db4 \u0dc3\u0dc4 \u0dc0\u0dd2\u0db1\u0dca\u0dba\u0dcf\u0dc3\u0dba\u0db1\u0dca</a>\u0db8\u0dad \u0db4\u0daf\u0db1\u0db8\u0dca \u0dc0\u0dda. </p>\n<p><a href=\"https://app.labml.ai/run/6348e504c3a511eba9529daa283fb495\"><span translate=no>_^_0_^_</span></a></p>\n",
"<h2>Configurations</h2>\n<p>This inherits from <a href=\"../../experiments/nlp_autoregression.html#NLPAutoRegressionConfigs\"><span translate=no>_^_0_^_</span></a></p>\n": "<h2>\u0dc0\u0dd2\u0db1\u0dca\u0dba\u0dcf\u0dc3\u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dca</h2>\n<p>\u0db8\u0dd9\u0dba\u0d8b\u0dbb\u0dd4\u0db8 \u0dc0\u0db1\u0dca\u0db1\u0dda <a href=\"../../experiments/nlp_autoregression.html#NLPAutoRegressionConfigs\"><span translate=no>_^_0_^_</span></a></p>\n",
"<h2>Simple autoregressive model</h2>\n<p>This consists of a token embedding layer, transformer encoder, and a final linear layer that gives token logits.</p>\n": "<h2>\u0dc3\u0dbb\u0dbd\u0dc3\u0dca\u0dc0\u0dba\u0d82\u0d9a\u0dca\u0dbb\u0dd3\u0dba \u0db4\u0dca\u0dbb\u0dad\u0dd2\u0d9c\u0dcf\u0db8\u0dd3 \u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba</h2>\n<p>\u0db8\u0dd9\u0dba\u0da7\u0ddd\u0d9a\u0db1\u0dca \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dda \u0dc3\u0dca\u0dae\u0dbb\u0dba\u0d9a\u0dca, \u0da7\u0dca\u0dbb\u0dcf\u0db1\u0dca\u0dc3\u0dca\u0dc6\u0ddd\u0db8\u0dbb\u0dca \u0d91\u0db1\u0dca\u0d9a\u0ddd\u0da9\u0dbb\u0dba\u0d9a\u0dca \u0dc3\u0dc4 \u0da7\u0ddd\u0d9a\u0db1\u0dca \u0db4\u0dd2\u0dc0\u0dd2\u0dc3\u0dd4\u0db8\u0dca \u0dbd\u0db6\u0dcf \u0daf\u0dd9\u0db1 \u0d85\u0dc0\u0dc3\u0dcf\u0db1 \u0dbb\u0dda\u0d9b\u0dd3\u0dba \u0dc3\u0dca\u0dae\u0dbb\u0dba\u0d9a\u0dd2\u0db1\u0dca \u0dc3\u0db8\u0db1\u0dca\u0dc0\u0dd2\u0dad \u0dc0\u0dda. </p>\n",
"<h3>Transformer configurations</h3>\n": "<h3>\u0da7\u0dca\u0dbb\u0dcf\u0db1\u0dca\u0dc3\u0dca\u0dc6\u0ddd\u0db8\u0dbb\u0dca\u0dc0\u0dd2\u0db1\u0dca\u0dba\u0dcf\u0dc3\u0dba\u0db1\u0dca</h3>\n",
"<p> </p>\n": "<p> </p>\n",
"<p> Create an auto-regressive model</p>\n": "<p> \u0dc3\u0dca\u0dc0\u0dba\u0d82\u0d9a\u0dca\u0dbb\u0dd3\u0dba\u0db4\u0dca\u0dbb\u0dad\u0dd2\u0d9c\u0dcf\u0db8\u0dd3 \u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba\u0d9a\u0dca \u0dc3\u0dcf\u0daf\u0db1\u0dca\u0db1</p>\n",
"<p>Batch size <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0d9a\u0dab\u0dca\u0da9\u0dcf\u0dba\u0db8\u0dca\u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba <span translate=no>_^_0_^_</span> </p>\n",
"<p>Create configs </p>\n": "<p>\u0dc0\u0dd2\u0db1\u0dca\u0dba\u0dcf\u0dc3\u0dc3\u0dcf\u0daf\u0db1\u0dca\u0db1 </p>\n",
"<p>Create experiment </p>\n": "<p>\u0d85\u0dad\u0dca\u0dc4\u0daf\u0dcf\u0db6\u0dd0\u0dbd\u0dd3\u0db8 \u0dc3\u0dcf\u0daf\u0db1\u0dca\u0db1 </p>\n",
"<p>Create subsequent mask if mask is not initialized or if the size of the mask is different </p>\n": "<p>\u0dc0\u0dd9\u0dc3\u0dca\u0db8\u0dd4\u0dc4\u0dd4\u0dab\u0d86\u0dbb\u0db8\u0dca\u0db7 \u0d9a\u0dbb \u0db1\u0ddc\u0db8\u0dd0\u0dad\u0dd2 \u0db1\u0db8\u0dca \u0dc4\u0ddd \u0dc0\u0dd9\u0dc3\u0dca \u0db8\u0dd4\u0dc4\u0dd4\u0dab\u0dd9\u0dc4\u0dd2 \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba \u0dc0\u0dd9\u0db1\u0dc3\u0dca \u0db1\u0db8\u0dca \u0db4\u0dc3\u0dd4\u0d9a\u0dcf\u0dbd\u0dd3\u0db1 \u0dc0\u0dd9\u0dc3\u0dca\u0db8\u0dd4\u0dc4\u0dd4\u0dab\u0d9a\u0dca \u0dc3\u0dcf\u0daf\u0db1\u0dca\u0db1 </p>\n",
"<p>Embedding size </p>\n": "<p>\u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba </p>\n",
"<p>FFN hidden dimension size </p>\n": "<p>FFN\u0dc3\u0dd0\u0d9f\u0dc0\u0dd4\u0dab\u0dd4 \u0db8\u0dcf\u0db1\u0dba\u0db1\u0dca \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba </p>\n",
"<p>GPT model </p>\n": "<p>GPT\u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba </p>\n",
"<p>Get logits </p>\n": "<p>\u0db4\u0dd2\u0dc0\u0dd2\u0dc3\u0dd4\u0db8\u0dca\u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1 </p>\n",
"<p>Get the token embeddings with positional encodings </p>\n": "<p>\u0dc3\u0dca\u0dae\u0dcf\u0db1\u0dd3\u0dba\u0d9a\u0dda\u0dad\u0db1 \u0d9a\u0dca\u0dbb\u0db8 \u0dc3\u0db8\u0d9f \u0da7\u0ddd\u0d9a\u0db1\u0dca \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1 </p>\n",
"<p>Optimizer </p>\n": "<p>\u0db4\u0dca\u0dbb\u0dc1\u0dc3\u0dca\u0dad\u0d9a\u0dbb\u0dab\u0dba </p>\n",
"<p>Override configurations </p>\n": "<p>\u0dc0\u0dd2\u0db1\u0dca\u0dba\u0dcf\u0dc3\u0dba\u0db1\u0dca\u0d85\u0db7\u0dd2\u0db6\u0dc0\u0dcf \u0dba\u0db1\u0dca\u0db1 </p>\n",
"<p>Prompt separator is blank </p>\n": "<p>\u0d9a\u0da9\u0dd2\u0db1\u0db8\u0dca\u0db6\u0dd9\u0daf\u0dd4\u0db8\u0dca\u0d9a\u0dbb\u0dd4 \u0dc4\u0dd2\u0dc3\u0dca \u0dba </p>\n",
"<p>Replace self-attention with an <a href=\"index.html\">AFT Local Module</a> </p>\n": "<p><a href=\"index.html\">AFT \u0daf\u0dda\u0dc1\u0dd3\u0dba \u0db8\u0ddc\u0da9\u0dd2\u0dba\u0dd4\u0dbd\u0dba\u0d9a\u0dca</a> \u0dc3\u0db8\u0d9f \u0dc3\u0dca\u0dc0\u0dba\u0d82 \u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0db4\u0dca\u0dbb\u0dad\u0dd2\u0dc3\u0dca\u0dae\u0dcf\u0db4\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1 </p>\n",
"<p>Return results (second value is for state, since our trainer is used with RNNs also) </p>\n": "<p>\u0db4\u0dca\u0dbb\u0dad\u0dd2\u0dbd\u0dcf\u0db7\u0db4\u0dca\u0dbb\u0dad\u0dd2 results \u0dbd (\u0daf\u0dd9\u0dc0\u0db1 \u0d85\u0d9c\u0dba \u0dbb\u0dcf\u0da2\u0dca\u0dba \u0dc3\u0db3\u0dc4\u0dcf \u0dc0\u0dda, \u0db8\u0db1\u0dca\u0daf \u0d85\u0db4\u0d9c\u0dda \u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4\u0d9a\u0dbb\u0dd4 RNs \u0dc3\u0db8\u0d9f \u0daf \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0dba\u0dd2) </p>\n",
"<p>Run training </p>\n": "<p>\u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4\u0db0\u0dcf\u0dc0\u0db1\u0dba </p>\n",
"<p>Set models for saving and loading </p>\n": "<p>\u0d89\u0dad\u0dd2\u0dbb\u0dd2\u0d9a\u0dd2\u0dbb\u0dd3\u0db8 \u0dc3\u0dc4 \u0db4\u0dd0\u0da7\u0dc0\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0d86\u0d9a\u0dd8\u0dad\u0dd2 \u0dc3\u0d9a\u0dc3\u0db1\u0dca\u0db1 </p>\n",
"<p>Set the embedding size </p>\n": "<p>\u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba \u0dc3\u0d9a\u0dc3\u0db1\u0dca\u0db1 </p>\n",
"<p>Set the vocabulary sizes for embeddings and generating logits </p>\n": "<p>\u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca\u0dc3\u0dc4 \u0db4\u0dd2\u0dc0\u0dd2\u0dc3\u0dd4\u0db8\u0dca \u0d8b\u0dad\u0dca\u0db4\u0dcf\u0daf\u0db1\u0dba \u0dc3\u0db3\u0dc4\u0dcf \u0dc0\u0da0\u0db1 \u0db8\u0dcf\u0dbd\u0dcf\u0dc0 \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab \u0dc3\u0d9a\u0dc3\u0db1\u0dca\u0db1 </p>\n",
"<p>Start the experiment </p>\n": "<p>\u0d85\u0dad\u0dca\u0dc4\u0daf\u0dcf\u0db6\u0dd0\u0dbd\u0dd3\u0db8 \u0d86\u0dbb\u0db8\u0dca\u0db7 \u0d9a\u0dbb\u0db1\u0dca\u0db1 </p>\n",
"<p>Starting prompt for sampling </p>\n": "<p>\u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd3\u0db8\u0dc3\u0db3\u0dc4\u0dcf \u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8\u0d9a\u0dca \u0d86\u0dbb\u0db8\u0dca\u0db7 \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 </p>\n",
"<p>Subsequent mask, will mask out tokens from seeing future tokens </p>\n": "<p>\u0db4\u0dc3\u0dd4\u0d9a\u0dcf\u0dbd\u0dd3\u0db1\u0dc0\u0dd9\u0dc3\u0dca\u0db8\u0dd4\u0dc4\u0dd4\u0dab, \u0d85\u0db1\u0dcf\u0d9c\u0dad \u0da7\u0ddd\u0d9a\u0db1 \u0daf\u0dd0\u0d9a\u0dd3\u0db8\u0dd9\u0db1\u0dca \u0da7\u0ddd\u0d9a\u0db1 \u0dc0\u0dc3\u0d82 \u0d9a\u0dbb\u0db1\u0dd4 \u0d87\u0dad </p>\n",
"<p>Switch between training and validation for <span translate=no>_^_0_^_</span> times per epoch </p>\n": "<p>\u0d91\u0d9a\u0dca <span translate=no>_^_0_^_</span> \u0dba\u0dd4\u0d9c\u0dba\u0d9a\u0da7 \u0dc0\u0dbb\u0d9a\u0dca \u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4\u0dc0 \u0dc3\u0dc4 \u0dc0\u0dbd\u0d82\u0d9c\u0dd4 \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 \u0d85\u0dad\u0dbb \u0db8\u0dcf\u0dbb\u0dd4 \u0dc0\u0db1\u0dca\u0db1 </p>\n",
"<p>The mask will be initialized on the first call </p>\n": "<p>\u0db4\u0dc5\u0db8\u0dd4\u0d87\u0db8\u0dad\u0dd4\u0db8\u0dd9\u0db1\u0dca \u0dc0\u0dd9\u0dc3\u0dca\u0db8\u0dd4\u0dc4\u0dd4\u0dab \u0d86\u0dbb\u0db8\u0dca\u0db7 \u0d9a\u0dbb\u0db1\u0dd4 \u0d87\u0dad </p>\n",
"<p>Train for <span translate=no>_^_0_^_</span> epochs </p>\n": "<p><span translate=no>_^_0_^_</span> Epochs \u0dc3\u0db3\u0dc4\u0dcf \u0daf\u0dd4\u0db8\u0dca\u0dbb\u0dd2\u0dba </p>\n",
"<p>Transformer </p>\n": "<p>\u0da7\u0dca\u0dbb\u0dcf\u0db1\u0dca\u0dc3\u0dca\u0dc6\u0ddd\u0db8\u0dbb\u0dca </p>\n",
"<p>Transformer encoder </p>\n": "<p>\u0da7\u0dca\u0dbb\u0dcf\u0db1\u0dca\u0dc3\u0dca\u0dc6\u0ddd\u0db8\u0dbb\u0dca\u0d91\u0db1\u0dca\u0d9a\u0ddd\u0da9\u0dbb\u0dba </p>\n",
"<p>Use Tiny Shakespeare dataset </p>\n": "<p>\u0d9a\u0dd4\u0da9\u0dcf\u0dc2\u0dda\u0d9a\u0dca\u0dc3\u0dca\u0db4\u0dd2\u0dba\u0dbb\u0dca \u0daf\u0dad\u0dca\u0dad \u0d9a\u0da7\u0dca\u0da7\u0dbd\u0dba \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0db1\u0dca\u0db1 </p>\n",
"<p>Use a context size of <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0d9a\u0dc3\u0db1\u0dca\u0daf\u0dbb\u0dca\u0db7\u0dba \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf <span translate=no>_^_0_^_</span> </p>\n",
"<p>Use character level tokenizer </p>\n": "<p>\u0d85\u0d9a\u0dca\u0dc2\u0dbb\u0db8\u0da7\u0dca\u0da7\u0db8\u0dda \u0da7\u0ddd\u0d9a\u0db1\u0dba\u0dd2\u0dc3\u0dbb\u0dca \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0db1\u0dca\u0db1 </p>\n",
"<p>We use our <a href=\"../configs.html#TransformerConfigs\">configurable transformer implementation</a> </p>\n": "<p>\u0d85\u0db4\u0d9c\u0dda <a href=\"../configs.html#TransformerConfigs\">\u0dc0\u0dd2\u0db1\u0dca\u0dba\u0dcf\u0dc3\u0d9c\u0dad \u0d9a\u0dc5 \u0dc4\u0dd0\u0d9a\u0dd2 \u0da7\u0dca\u0dbb\u0dcf\u0db1\u0dca\u0dc3\u0dca\u0dc6\u0ddd\u0db8\u0dbb\u0dca \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dd2\u0dbb\u0dd3\u0db8</a> \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0db8\u0dd4 </p>\n",
"<ul><li><span translate=no>_^_0_^_</span> is the transformer <a href=\"../models.html#Encoder\">Encoder</a> </li>\n<li><span translate=no>_^_1_^_</span> is the token <a href=\"../models.html#EmbeddingsWithLearnedPositionalEncoding\">embedding module (with positional encodings)</a> </li>\n<li><span translate=no>_^_2_^_</span> is the <a href=\"../models.html#Generator\">final fully connected layer</a> that gives the logits.</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span> \u0da7\u0dca\u0dbb\u0dcf\u0db1\u0dca\u0dc3\u0dca\u0dc6\u0ddd\u0db8\u0dbb\u0dca <a href=\"../models.html#Encoder\">\u0d91\u0db1\u0dca\u0d9a\u0ddd\u0da9\u0dbb\u0dba</a> </li>\n<li><span translate=no>_^_1_^_</span> \u0dba\u0db1\u0dd4 \u0da7\u0ddd\u0d9a\u0db1\u0dca <a href=\"../models.html#EmbeddingsWithLearnedPositionalEncoding\">\u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dda \u0db8\u0ddc\u0da9\u0dd2\u0dba\u0dd4\u0dbd\u0dba (\u0dc3\u0dca\u0dae\u0dcf\u0db1\u0dd3\u0dba \u0d9a\u0dda\u0dad\u0dd3\u0d9a\u0dbb\u0dab \u0dc3\u0db8\u0d9f)</a> </li>\n<li><span translate=no>_^_2_^_</span> \u0dba\u0db1\u0dd4 \u0db4\u0dd2\u0dc0\u0dd2\u0dc3\u0dd4\u0db8\u0dca \u0dbd\u0db6\u0dcf \u0daf\u0dd9\u0db1 <a href=\"../models.html#Generator\">\u0d85\u0dc0\u0dc3\u0dcf\u0db1 \u0db4\u0dd6\u0dbb\u0dca\u0dab \u0dc3\u0db8\u0dca\u0db6\u0db1\u0dca\u0db0\u0dd2\u0dad \u0dc3\u0dca\u0dae\u0dbb\u0dba\u0dba\u0dd2</a> . </li></ul>\n",
"Attention Free Transformer (AFT) Experiment": "\u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0db1\u0dd2\u0daf\u0dc4\u0dc3\u0dca \u0da7\u0dca\u0dbb\u0dcf\u0db1\u0dca\u0dc3\u0dca\u0dc6\u0ddd\u0db8\u0dbb\u0dca (AFT) \u0d85\u0dad\u0dca\u0dc4\u0daf\u0dcf \u0db6\u0dd0\u0dbd\u0dd3\u0db8",
"This experiment trains an Attention Free Transformer (AFT) based model on Tiny Shakespeare dataset.": "\u0db8\u0dd9\u0db8 \u0d85\u0dad\u0dca\u0dc4\u0daf\u0dcf \u0db6\u0dd0\u0dbd\u0dd3\u0db8 \u0d9a\u0dd4\u0da9\u0dcf \u0dc2\u0dda\u0d9a\u0dca\u0dc3\u0dca\u0db4\u0dd2\u0dba\u0dbb\u0dca \u0daf\u0dad\u0dca\u0dad \u0d9a\u0da7\u0dca\u0da7\u0dbd\u0dba \u0db8\u0dad \u0db4\u0daf\u0db1\u0db8\u0dca \u0dc0\u0dd6 \u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0dbb\u0dc4\u0dd2\u0dad \u0da7\u0dca\u0dbb\u0dcf\u0db1\u0dca\u0dc3\u0dca\u0dc6\u0ddd\u0db8\u0dbb\u0dca (AFT) \u0db4\u0daf\u0db1\u0db8\u0dca \u0d9a\u0dbb\u0d9c\u0dad\u0dca \u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba\u0d9a\u0dca \u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4 \u0d9a\u0dbb\u0dba\u0dd2."
}
translate_cache/transformers/aft/experiment.zh.json
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{
"<h1><a href=\"index.html\">Attention Free Transformer (AFT)</a> Experiment</h1>\n<p>This is an annotated PyTorch experiment to train a <a href=\"index.html\">AFT model</a>.</p>\n<p>This is based on <a href=\"../../experiments/nlp_autoregression.html\">general training loop and configurations for auto-regressive NLP task</a>.</p>\n": "<h1><a href=\"index.html\">\u65e0\u6ce8\u610f\u53d8\u5f62\u91d1\u521a (AFT)</a> \u5b9e\u9a8c</h1>\n<p>\u8fd9\u662f\u4e00\u9879\u5e26\u6ce8\u91ca\u7684 PyTorch \u5b9e\u9a8c\uff0c\u7528\u4e8e\u8bad\u7ec3 A <a href=\"index.html\">FT \u6a21\u578b</a>\u3002</p>\n<p>\u8fd9\u662f\u57fa\u4e8e<a href=\"../../experiments/nlp_autoregression.html\">\u5e38\u89c4\u8bad\u7ec3\u5faa\u73af\u548c\u81ea\u56de\u5f52 NLP \u4efb\u52a1\u7684\u914d\u7f6e</a>\u3002</p>\n",
"<h2>Configurations</h2>\n<p>This inherits from <a href=\"../../experiments/nlp_autoregression.html#NLPAutoRegressionConfigs\"><span translate=no>_^_0_^_</span></a></p>\n": "<h2>\u914d\u7f6e</h2>\n<p>\u8fd9\u7ee7\u627f\u81ea <a href=\"../../experiments/nlp_autoregression.html#NLPAutoRegressionConfigs\"><span translate=no>_^_0_^_</span></a></p>\n",
"<h2>Simple autoregressive model</h2>\n<p>This consists of a token embedding layer, transformer encoder, and a final linear layer that gives token logits.</p>\n": "<h2>\u7b80\u5355\u7684\u81ea\u56de\u5f52\u6a21\u578b</h2>\n<p>\u8fd9\u5305\u62ec\u4ee4\u724c\u5d4c\u5165\u5c42\u3001\u53d8\u538b\u5668\u7f16\u7801\u5668\u548c\u7ed9\u51fa\u4ee4\u724c\u65e5\u5fd7\u7684\u6700\u7ec8\u7ebf\u6027\u5c42\u3002</p>\n",
"<h3>Transformer configurations</h3>\n": "<h3>\u53d8\u538b\u5668\u914d\u7f6e</h3>\n",
"<p> </p>\n": "<p></p>\n",
"<p> Create an auto-regressive model</p>\n": "<p>\u521b\u5efa\u81ea\u52a8\u56de\u5f52\u6a21\u578b</p>\n",
"<p>Batch size <span translate=no>_^_0_^_</span> </p>\n": "<p>\u6279\u91cf\u5927\u5c0f<span translate=no>_^_0_^_</span></p>\n",
"<p>Create configs </p>\n": "<p>\u521b\u5efa\u914d\u7f6e</p>\n",
"<p>Create experiment </p>\n": "<p>\u521b\u5efa\u5b9e\u9a8c</p>\n",
"<p>Create subsequent mask if mask is not initialized or if the size of the mask is different </p>\n": "<p>\u5982\u679c\u63a9\u7801\u672a\u521d\u59cb\u5316\u6216\u63a9\u7801\u5927\u5c0f\u4e0d\u540c\uff0c\u5219\u521b\u5efa\u540e\u7eed\u63a9\u7801</p>\n",
"<p>Embedding size </p>\n": "<p>\u5d4c\u5165\u5927\u5c0f</p>\n",
"<p>FFN hidden dimension size </p>\n": "<p>FFN \u9690\u85cf\u5c3a\u5bf8\u5927\u5c0f</p>\n",
"<p>GPT model </p>\n": "<p>GPT \u578b\u53f7</p>\n",
"<p>Get logits </p>\n": "<p>\u83b7\u53d6\u65e5\u5fd7</p>\n",
"<p>Get the token embeddings with positional encodings </p>\n": "<p>\u4f7f\u7528\u4f4d\u7f6e\u7f16\u7801\u83b7\u53d6\u4ee4\u724c\u5d4c\u5165</p>\n",
"<p>Optimizer </p>\n": "<p>\u4f18\u5316\u5668</p>\n",
"<p>Override configurations </p>\n": "<p>\u8986\u76d6\u914d\u7f6e</p>\n",
"<p>Prompt separator is blank </p>\n": "<p>\u63d0\u793a\u5206\u9694\u7b26\u4e3a\u7a7a</p>\n",
"<p>Replace self-attention with an <a href=\"index.html\">AFT Local Module</a> </p>\n": "<p>\u7528 <a href=\"index.html\">AFT \u672c\u5730\u6a21\u5757</a>\u66ff\u6362\u81ea\u6211\u6ce8\u610f</p>\n",
"<p>Return results (second value is for state, since our trainer is used with RNNs also) </p>\n": "<p>\u8fd4\u56de\u7ed3\u679c\uff08\u7b2c\u4e8c\u4e2a\u503c\u7528\u4e8e\u72b6\u6001\uff0c\u56e0\u4e3a\u6211\u4eec\u7684\u8bad\u7ec3\u5668\u4e5f\u4e0e RNN \u4e00\u8d77\u4f7f\u7528\uff09</p>\n",
"<p>Run training </p>\n": "<p>\u8dd1\u6b65\u8bad\u7ec3</p>\n",
"<p>Set models for saving and loading </p>\n": "<p>\u8bbe\u7f6e\u7528\u4e8e\u4fdd\u5b58\u548c\u52a0\u8f7d\u7684\u6a21\u578b</p>\n",
"<p>Set the embedding size </p>\n": "<p>\u8bbe\u7f6e\u5d4c\u5165\u5927\u5c0f</p>\n",
"<p>Set the vocabulary sizes for embeddings and generating logits </p>\n": "<p>\u8bbe\u7f6e\u5d4c\u5165\u548c\u751f\u6210 logit \u7684\u8bcd\u6c47\u91cf\u5927\u5c0f</p>\n",
"<p>Start the experiment </p>\n": "<p>\u5f00\u59cb\u5b9e\u9a8c</p>\n",
"<p>Starting prompt for sampling </p>\n": "<p>\u5f00\u59cb\u91c7\u6837\u63d0\u793a</p>\n",
"<p>Subsequent mask, will mask out tokens from seeing future tokens </p>\n": "<p>\u540e\u7eed\u7684\u63a9\u7801\uff0c\u5c06\u63a9\u76d6\u4ee4\u724c\u4ee5\u514d\u770b\u5230\u672a\u6765\u7684\u4ee3\u5e01</p>\n",
"<p>Switch between training and validation for <span translate=no>_^_0_^_</span> times per epoch </p>\n": "<p>\u5728\u8bad\u7ec3\u548c\u9a8c\u8bc1\u4e4b\u95f4\u5207\u6362\u6bcf\u4e2a\u7eaa\u5143\u7684<span translate=no>_^_0_^_</span>\u6b21\u6570</p>\n",
"<p>The mask will be initialized on the first call </p>\n": "<p>\u63a9\u7801\u5c06\u5728\u7b2c\u4e00\u6b21\u8c03\u7528\u65f6\u521d\u59cb\u5316</p>\n",
"<p>Train for <span translate=no>_^_0_^_</span> epochs </p>\n": "<p>\u4e3a<span translate=no>_^_0_^_</span>\u65f6\u4ee3\u800c\u8bad\u7ec3</p>\n",
"<p>Transformer </p>\n": "<p>\u53d8\u538b\u5668</p>\n",
"<p>Transformer encoder </p>\n": "<p>\u53d8\u538b\u5668\u7f16\u7801</p>\n",
"<p>Use Tiny Shakespeare dataset </p>\n": "<p>\u4f7f\u7528\u5c0f\u838e\u58eb\u6bd4\u4e9a\u6570\u636e\u96c6</p>\n",
"<p>Use a context size of <span translate=no>_^_0_^_</span> </p>\n": "<p>\u4f7f\u7528\u4e0a\u4e0b\u6587\u5927\u5c0f\u4e3a<span translate=no>_^_0_^_</span></p>\n",
"<p>Use character level tokenizer </p>\n": "<p>\u4f7f\u7528\u89d2\u8272\u7b49\u7ea7\u5206\u8bcd\u5668</p>\n",
"<p>We use our <a href=\"../configs.html#TransformerConfigs\">configurable transformer implementation</a> </p>\n": "<p>\u6211\u4eec\u4f7f\u7528\u6211\u4eec\u7684<a href=\"../configs.html#TransformerConfigs\">\u53ef\u914d\u7f6e\u53d8\u538b\u5668\u5b9e\u73b0</a></p>\n",
"<ul><li><span translate=no>_^_0_^_</span> is the transformer <a href=\"../models.html#Encoder\">Encoder</a> </li>\n<li><span translate=no>_^_1_^_</span> is the token <a href=\"../models.html#EmbeddingsWithLearnedPositionalEncoding\">embedding module (with positional encodings)</a> </li>\n<li><span translate=no>_^_2_^_</span> is the <a href=\"../models.html#Generator\">final fully connected layer</a> that gives the logits.</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u53d8\u538b\u5668<a href=\"../models.html#Encoder\">\u7f16\u7801\u5668</a></li>\n<li><span translate=no>_^_1_^_</span>\u662f\u4ee4\u724c<a href=\"../models.html#EmbeddingsWithLearnedPositionalEncoding\">\u5d4c\u5165\u6a21\u5757\uff08\u5e26\u6709\u4f4d\u7f6e\u7f16\u7801\uff09</a></li>\n<li><span translate=no>_^_2_^_</span>\u662f\u7ed9<a href=\"../models.html#Generator\">\u51fa logit \u7684\u6700\u540e\u4e00\u4e2a\u5b8c\u5168\u8fde\u63a5\u7684\u5c42</a>\u3002</li></ul>\n",
"Attention Free Transformer (AFT) Experiment": "\u514d\u6ce8\u610f\u53d8\u538b\u5668 (AFT) \u5b9e\u9a8c",
"This experiment trains an Attention Free Transformer (AFT) based model on Tiny Shakespeare dataset.": "\u672c\u5b9e\u9a8c\u5728 Tiny Shakespeare \u6570\u636e\u96c6\u4e0a\u8bad\u7ec3\u57fa\u4e8e\u65e0\u6ce8\u610f\u529b\u53d8\u538b\u5668\uff08AFT\uff09\u7684\u6a21\u578b\u3002"
}
translate_cache/transformers/aft/readme.ja.json
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{
"<h1><a href=\"https://nn.labml.ai/transformers/aft/index.html\">An Attention Free Transformer</a></h1>\n<p>This is a <a href=\"https://pytorch.org\">PyTorch</a> implementation of the paper <a href=\"https://arxiv.org/abs/2105.14103\">An Attention Free Transformer</a>.</p>\n<p>This paper replaces the <a href=\"https://nn.labml.ai/transformers/mha.html\">self-attention layer</a> with a new efficient operation, that has memory complexity of O(Td), where T is the sequence length and <span translate=no>_^_0_^_</span> is the dimensionality of embeddings.</p>\n<p>The paper introduces AFT along with AFT-local and AFT-conv. Here we have implemented AFT-local which pays attention to closeby tokens in an autoregressive model. </p>\n": "<h1><a href=\"https://nn.labml.ai/transformers/aft/index.html\">\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30d5\u30ea\u30fc\u306e\u30c8\u30e9\u30f3\u30b9\u30d5\u30a9\u30fc\u30de\u30fc</a></h1>\n<p>\u3053\u308c\u306f\u3001\u8ad6\u6587\u300c<a href=\"https://arxiv.org/abs/2105.14103\">\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30fb\u30d5\u30ea\u30fc\u30fb\u30c8\u30e9\u30f3\u30b9\u30d5\u30a9\u30fc\u30de\u30fc\u300d<a href=\"https://pytorch.org\">\u3092PyTorch\u3067\u5b9f\u88c5\u3057\u305f\u3082\u306e\u3067\u3059</a></a>\u3002</p>\n<p>\u3053\u306e\u8ad6\u6587\u3067\u306f\u3001<a href=\"https://nn.labml.ai/transformers/mha.html\">\u30bb\u30eb\u30d5\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u5c64\u3092\u65b0\u3057\u3044\u52b9\u7387\u7684\u306a\u6f14\u7b97\u306b\u7f6e\u304d\u63db\u3048\u307e\u3059</a>\u3002\u30e1\u30e2\u30ea\u8907\u96d1\u5ea6\u306f O (Td) \u3067\u3001T \u306f\u30b7\u30fc\u30b1\u30f3\u30b9\u306e\u9577\u3055\u3067\u3001<span translate=no>_^_0_^_</span>\u57cb\u3081\u8fbc\u307f\u306e\u6b21\u5143\u3067\u3059\u3002</p>\n<p>\u3053\u306e\u8ad6\u6587\u3067\u306f\u3001AFT\u3068AFT\u30ed\u30fc\u30ab\u30eb\u304a\u3088\u3073AFT-Conv\u306b\u3064\u3044\u3066\u7d39\u4ecb\u3057\u3066\u3044\u307e\u3059\u3002\u3053\u3053\u3067\u306f\u3001\u81ea\u5df1\u56de\u5e30\u30e2\u30c7\u30eb\u3067\u8fd1\u304f\u306e\u30c8\u30fc\u30af\u30f3\u306b\u6ce8\u76ee\u3059\u308bAFT-Local\u3092\u5b9f\u88c5\u3057\u307e\u3057\u305f</p>\u3002\n",
"An Attention Free Transformer": "\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30d5\u30ea\u30fc\u306e\u30c8\u30e9\u30f3\u30b9\u30d5\u30a9\u30fc\u30de\u30fc"
}
translate_cache/transformers/aft/readme.si.json
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{
"<h1><a href=\"https://nn.labml.ai/transformers/aft/index.html\">An Attention Free Transformer</a></h1>\n<p>This is a <a href=\"https://pytorch.org\">PyTorch</a> implementation of the paper <a href=\"https://arxiv.org/abs/2105.14103\">An Attention Free Transformer</a>.</p>\n<p>This paper replaces the <a href=\"https://nn.labml.ai/transformers/mha.html\">self-attention layer</a> with a new efficient operation, that has memory complexity of O(Td), where T is the sequence length and <span translate=no>_^_0_^_</span> is the dimensionality of embeddings.</p>\n<p>The paper introduces AFT along with AFT-local and AFT-conv. Here we have implemented AFT-local which pays attention to closeby tokens in an autoregressive model.</p>\n<p><a href=\"https://app.labml.ai/run/6348e504c3a511eba9529daa283fb495\"><span translate=no>_^_1_^_</span></a> </p>\n": "<h1><a href=\"https://nn.labml.ai/transformers/aft/index.html\">\u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0dbb\u0dc4\u0dd2\u0dad \u0da7\u0dca\u0dbb\u0dcf\u0db1\u0dca\u0dc3\u0dca\u0dc6\u0ddd\u0db8\u0dbb\u0dba\u0d9a\u0dca</a></h1>\n<p>\u0db8\u0dd9\u0dba <a href=\"https://pytorch.org\">PyTorch</a> \u0d9a\u0da9\u0daf\u0dcf\u0dc3\u0dd2 \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a <a href=\"https://arxiv.org/abs/2105.14103\">\u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0d9a\u0dd2 \u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0db1\u0dd2\u0daf\u0dc4\u0dc3\u0dca \u0da7\u0dca\u0dbb\u0dcf\u0db1\u0dca\u0dc3\u0dca\u0dc6\u0ddd\u0db8\u0dbb\u0dca</a> . </p>\n<p>\u0db8\u0dd9\u0db8\u0dbd\u0dd2\u0db4\u0dd2\u0dba <a href=\"https://nn.labml.ai/transformers/mha.html\">\u0dc3\u0dca\u0dc0\u0dba\u0d82 \u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0dc3\u0dca\u0dad\u0dbb\u0dba</a> \u0db1\u0dc0 \u0d9a\u0dcf\u0dbb\u0dca\u0dba\u0d9a\u0dca\u0dc2\u0db8 \u0db8\u0dd9\u0dc4\u0dd9\u0dba\u0dd4\u0db8\u0d9a\u0dd2\u0db1\u0dca \u0db4\u0dca\u0dbb\u0dad\u0dd2\u0dc3\u0dca\u0dae\u0dcf\u0db4\u0db1\u0dba \u0d9a\u0dbb\u0dba\u0dd2, \u0d91\u0dba O (Td) \u0dc4\u0dd2 \u0db8\u0dad\u0d9a \u0dc3\u0d82\u0d9a\u0dd3\u0dbb\u0dca\u0dab\u0dad\u0dcf\u0dc0\u0dba\u0d9a\u0dca \u0d87\u0dad\u0dd2 \u0d85\u0dad\u0dbb \u0da7\u0dd3 \u0dba\u0db1\u0dd4 \u0d85\u0db1\u0dd4\u0d9a\u0dca\u0dbb\u0db8\u0dba\u0dda \u0daf\u0dd2\u0d9c <span translate=no>_^_0_^_</span> \u0dc0\u0db1 \u0d85\u0dad\u0dbb \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0dc0\u0dbd \u0db8\u0dcf\u0db1\u0dba\u0db1\u0dca \u0dc0\u0dda. </p>\n<p>\u0d9a\u0da9\u0daf\u0dcf\u0dc3\u0dd2AFT \u0dc3\u0dc4 AFT \u0dc4\u0db3\u0dd4\u0db1\u0dca\u0dc0\u0dcf \u0daf\u0dd9\u0dba\u0dd2 AFT \u0dc3\u0dc4 AFT-conv. \u0db8\u0dd9\u0db1\u0dca\u0db1 \u0d85\u0db4\u0dd2 \u0dc3\u0dca\u0dc0\u0dba\u0d82\u0d9a\u0dca\u0dbb\u0dd3\u0dba \u0db4\u0dca\u0dbb\u0dad\u0dd2\u0d9c\u0dcf\u0db8\u0dd3 \u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba\u0d9a\u0dca \u0dad\u0dd4\u0dc5 \u0dc3\u0db8\u0dd3\u0db4 \u0da7\u0ddd\u0d9a\u0db1 \u0d9a\u0dd9\u0dbb\u0dd9\u0dc4\u0dd2 \u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0dba\u0ddc\u0db8\u0dd4 \u0d9a\u0dbb\u0db1 AFT- \u0daf\u0dda\u0dc1\u0dd3\u0dba \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dbb \u0d87\u0dad\u0dca\u0dad\u0dd9\u0db8\u0dd4. </p>\n<p><a href=\"https://app.labml.ai/run/6348e504c3a511eba9529daa283fb495\"><span translate=no>_^_1_^_</span></a> </p>\n",
"An Attention Free Transformer": "\u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0dbb\u0dc4\u0dd2\u0dad \u0da7\u0dca\u0dbb\u0dcf\u0db1\u0dca\u0dc3\u0dca\u0dc6\u0ddd\u0db8\u0dbb\u0dba\u0d9a\u0dca"
}
translate_cache/transformers/aft/readme.zh.json
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{
"<h1><a href=\"https://nn.labml.ai/transformers/aft/index.html\">An Attention Free Transformer</a></h1>\n<p>This is a <a href=\"https://pytorch.org\">PyTorch</a> implementation of the paper <a href=\"https://arxiv.org/abs/2105.14103\">An Attention Free Transformer</a>.</p>\n<p>This paper replaces the <a href=\"https://nn.labml.ai/transformers/mha.html\">self-attention layer</a> with a new efficient operation, that has memory complexity of O(Td), where T is the sequence length and <span translate=no>_^_0_^_</span> is the dimensionality of embeddings.</p>\n<p>The paper introduces AFT along with AFT-local and AFT-conv. Here we have implemented AFT-local which pays attention to closeby tokens in an autoregressive model. </p>\n": "<h1><a href=\"https://nn.labml.ai/transformers/aft/index.html\">\u4e00\u79cd\u65e0\u6ce8\u610f\u529b\u7684 Transformer </a></h1>\n<p>\u8fd9\u662f\u8bba\u6587 <a href=\"https://arxiv.org/abs/2105.14103\">\u300a\u4e00\u79cd\u65e0\u6ce8\u610f\u529b\u7684 Transformer \u300b</a>\u7684<a href=\"https://pytorch.org\">PyTorch </a>\u5b9e\u73b0\u3002</p>\n<p>\u8fd9\u7bc7\u8bba\u6587\u7528\u4e00\u79cd\u65b0\u7684\u9ad8\u6548\u64cd\u4f5c\u66ff\u4ee3\u4e86<a href=\"https://nn.labml.ai/transformers/mha.html\">\u81ea\u6ce8\u610f\u529b\u5c42</a>\uff0c\u8be5\u8fd0\u7b97\u7684\u5b58\u50a8\u590d\u6742\u5ea6\u4e3aO\uff08Td\uff09\uff0c\u5176\u4e2d T \u662f\u5e8f\u5217\u957f\u5ea6\uff0c<span translate=no>_^_0_^_</span>\u662f\u5d4c\u5165\u7684\u7ef4\u5ea6\u3002</p>\n<p>\u8be5\u8bba\u6587\u4ecb\u7ecd\u4e86 AFT \u4ee5\u53ca AFT-local \u548c AFT-conv \u3002\u8fd9\u91cc\u6211\u4eec\u5b9e\u73b0\u4e86 AFT-local \uff0c\u5b83\u4f1a\u5728\u81ea\u56de\u5f52\u6a21\u578b\u4e2d\u5173\u6ce8\u90bb\u8fd1\u7684 token \u3002</p>\n",
"An Attention Free Transformer": "\u4e00\u79cd\u65e0\u6ce8\u610f\u529b\u7684 Transformer"
}