update doc

_build/html/_sources/index.rst.txt

@@ -65,9 +65,9 @@ Here is a simple python demo no how to use xLearn.
    ffm_model.setSigmoid()
    ffm_model.predict("./model.out", "./output.txt")  
 
-This example shows how to use xlearn to solve a simple binary classification task. 
-You can find the demo data **small_train.txt** and **small_test.txt** from 
-the **demo/classification/criteo_ctr/** directory.
+This example shows how to use field-aware factorizations machine (ffm) to solve a 
+simple binary classification task. You can check out the demo data 
+(``small_train.txt`` and ``small_test.txt``) from the path ``demo/classification/criteo_ctr``
 
 Other Helpful Resources
 --------------------------------------------

_build/html/index.html

@@ -216,9 +216,9 @@ <h2>Python Demo<a class="headerlink" href="#python-demo" title="Permalink to thi
 <span class="n">ffm_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="s2">&quot;./model.out&quot;</span><span class="p">,</span> <span class="s2">&quot;./output.txt&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<p>This example shows how to use xlearn to solve a simple binary classification task.
-You can find the demo data <strong>small_train.txt</strong> and <strong>small_test.txt</strong> from
-the <strong>demo/classification/criteo_ctr/</strong> directory.</p>
+<p>This example shows how to use field-aware factorizations machine (ffm) to solve a
+simple binary classification task. You can check out the demo data
+(<code class="docutils literal"><span class="pre">small_train.txt</span></code> and <code class="docutils literal"><span class="pre">small_test.txt</span></code>) from the path <code class="docutils literal"><span class="pre">demo/classification/criteo_ctr</span></code></p>
 </div>
 <div class="section" id="other-helpful-resources">
 <h2>Other Helpful Resources<a class="headerlink" href="#other-helpful-resources" title="Permalink to this headline">¶</a></h2>

index.rst

@@ -65,9 +65,9 @@ Here is a simple python demo no how to use xLearn.
    ffm_model.setSigmoid()
    ffm_model.predict("./model.out", "./output.txt")  
 
-This example shows how to use xlearn to solve a simple binary classification task. 
-You can find the demo data **small_train.txt** and **small_test.txt** from 
-the **demo/classification/criteo_ctr/** directory.
+This example shows how to use field-aware factorizations machine (ffm) to solve a 
+simple binary classification task. You can check out the demo data 
+(``small_train.txt`` and ``small_test.txt``) from the path ``demo/classification/criteo_ctr``
 
 Other Helpful Resources
 --------------------------------------------