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Sampling Techniques (#139)
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Varuna Jayasiri
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<h1>Greedy Sampling</h1>
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<p>Here we sample the most likely token from the distribution of logits.</p>
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<div class="highlight"><pre><span class="lineno">12</span><span></span><span class="kn">import</span> <span class="nn">torch</span>
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<span class="lineno">13</span>
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<span class="lineno">14</span><span class="kn">from</span> <span class="nn">labml_nn.sampling</span> <span class="kn">import</span> <span class="n">Sampler</span></pre></div>
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<div class="highlight"><pre><span class="lineno">17</span><span class="k">class</span> <span class="nc">GreedySampler</span><span class="p">(</span><span class="n">Sampler</span><span class="p">):</span></pre></div>
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<p> Sample the most likely token from the distribution of logits</p>
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<div class="highlight"><pre><span class="lineno">18</span> <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">logits</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span></pre></div>
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<div class="highlight"><pre><span class="lineno">22</span> <span class="k">return</span> <span class="n">logits</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></pre></div>
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