JasonWang
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fastText
镜像自地址 https://github.com/facebookresearch/fastText


			
				
					
						
						
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        </script></nav></div><div class="container mainContainer docsContainer"><div class="wrapper"><div class="post"><header class="postHeader"><h1 id="__docusaurus" class="postHeaderTitle">List of options</h1></header><article><div><span><p>Invoke a command without arguments to list available arguments and their default values:</p>
<pre><code class="hljs css language-bash">$ ./fasttext supervised
Empty input or output path.

The following arguments are mandatory:
  -input              training file path
  -output             output file path

  The following arguments are optional:
  -verbose            verbosity level [2]

  The following arguments <span class="hljs-keyword">for</span> the dictionary are optional:
  -minCount           minimal number of word occurrences [1]
  -minCountLabel      minimal number of label occurrences [0]
  -wordNgrams         max length of word ngram [1]
  -bucket             number of buckets [2000000]
  -minn               min length of char ngram [0]
  -maxn               max length of char ngram [0]
  -t                  sampling threshold [0.0001]
  -label              labels prefix [__label__]

  The following arguments <span class="hljs-keyword">for</span> training are optional:
  -lr                 learning rate [0.1]
  -lrUpdateRate       change the rate of updates <span class="hljs-keyword">for</span> the learning rate [100]
  -dim                size of word vectors [100]
  -ws                 size of the context window [5]
  -epoch              number of epochs [5]
  -neg                number of negatives sampled [5]
  -loss               loss <span class="hljs-keyword">function</span> {ns, hs, softmax} [softmax]
  -thread             number of threads [12]
  -pretrainedVectors  pretrained word vectors <span class="hljs-keyword">for</span> supervised learning []
  -saveOutput         whether output params should be saved [0]

  The following arguments <span class="hljs-keyword">for</span> quantization are optional:
  -cutoff             number of words and ngrams to retain [0]
  -retrain            finetune embeddings <span class="hljs-keyword">if</span> a cutoff is applied [0]
  -qnorm              quantizing the norm separately [0]
  -qout               quantizing the classifier [0]
  -dsub               size of each sub-vector [2]
</code></pre>
<p>Defaults may vary by mode. (Word-representation modes <code>skipgram</code> and <code>cbow</code> use a default <code>-minCount</code> of 5.)</p>
<p>Hyperparameter optimization (autotune) is activated when you provide a validation file with <code>-autotune-validation</code> argument.</p>
<pre><code class="hljs css language-text">The following arguments are for autotune:
  -autotune-validation            validation file to be used for evaluation
  -autotune-metric                metric objective {f1, f1:labelname} [f1]
  -autotune-predictions           number of predictions used for evaluation  [1]
  -autotune-duration              maximum duration in seconds [300]
  -autotune-modelsize             constraint model file size [] (empty = do not quantize)
</code></pre>
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