update

Author: Gordon119

docs/examples/plot_dataset_tutorial.py

@@ -62,7 +62,7 @@
 ###############################################################################
 # In this case, if you want to use a deep learning model,
 # use ``load_datasets`` from ``libmultilabel.nn.data_utils`` and change the data to the dataframes we created.
-# Here is the modification of our `Bert model quickstart <https://www.csie.ntu.edu.tw/~cjlin/libmultilabel/auto_examples/plot_BERT_quickstart.html>`_.
+# Here is the modification of our `Bert model quickstart <../auto_examples/plot_bert_quickstart.html>`_.
 
 from libmultilabel.nn.data_utils import load_datasets
 

docs/examples/plot_linear_gridsearch_tutorial.py

@@ -34,7 +34,7 @@
 # As for the estimator ``MultiLabelEstimator``, argument ``options`` is a LIBLINEAR option
 # (see *train Usage* in `liblinear <https://github.com/cjlin1/liblinear>`__ README), and
 # ``linear_technique`` is one of the linear techniques, including ``1vsrest``, ``thresholding``, ``cost_sensitive``,
-# ``cost_sensitive_micro``, and ``binary_and_mulitclass``.
+# ``cost_sensitive_micro``, and ``binary_and_multiclass``.
 #
 # We can specify the aliases of the components used by the pipeline.
 # For example, ``tfidf`` is the alias of ``TfidfVectorizer`` and ``clf`` is the alias of the estimator.

docs/examples/plot_linear_tree_tutorial.py

@@ -1,5 +1,5 @@
 """
-Handling Data with Many Labels using Linear Methods.
+Handling Data with Many Labels Using Linear Methods
 ====================================================
 
 For the case that the amount of labels is very large,

docs/examples/plot_multi_label.py

@@ -69,12 +69,12 @@
 #      - Training time (sec)
 #
 #    * - Linear method (one-vs-rest)
-#      - 0.5171960144875225
-#      - 4.327306747436523
+#      - 0.52
+#      - 4.33
 #
 #    * - Deep learning method (BERT)
-#      - 0.564618763137536
-#      - 5412.955321788788
+#      - 0.56
+#      - 5412.96
 #
 # Step 2. Training: 
 # -----------------
@@ -120,13 +120,13 @@
 #      - Macro-F1
 #
 #    * - One-vs-rest
-#      - 0.5171960144875225
+#      - 0.52
 #
 #    * - Thresholding
-#      - 0.5643407144065415
+#      - 0.56
 #
 #    * - Cost-sensitive
-#      - 0.5704056980791481
+#      - 0.57
 #
 # From the comparison, one can see that these techniques improves the naive method.
 #
@@ -139,7 +139,7 @@
 # Training models directly in this case may result in high runtime and space consumption. 
 # A solution to reduce these costs is to utilize tree-based models.
 # Here we provide an example comparing a linear one-vs-rest model and a tree model on the EUR-Lex-57k dataset, which has a larger label space. 
-# We start by training a tree model following another detailed `tutorial <../auto_examples/plot_linear_tree_tutorial.html>`__.
+# We start by training a tree model following the `linear tree tutorial <../auto_examples/plot_linear_tree_tutorial.html>`__.
 
 datasets_eurlex = linear.load_dataset("txt", "data/eurlex57k/train.txt", "data/eurlex57k/test.txt")
 preprocessor_eurlex = linear.Preprocessor()
@@ -168,7 +168,7 @@
 # 
 # It is clear that the tree model significantly improves efficiency.
 # As for deep learning, a similar improvement in efficiency can be observed. 
-# Details for the tree-based deep learning model can be found in this `tutorial <../tutorials/AttentionXML.html>`__.
+# Details for the tree-based deep learning model can be found in the `deep learning tree tutorial <../tutorials/AttentionXML.html>`__.
 #
 # Step 3. Evaluation: Pick Suitable Metrics
 # -----------------------------------------
@@ -203,8 +203,7 @@
 # -----------------------------
 # Models with suboptimal hyperparameters may lead to poor performance :cite:p:`JJL21a`.
 # Users can incorporate hyperparameter tuning into the training process.
-# Because this functionality is more complex and cannot be adequately demonstrated within a code snippet, please refer to these two tutorials for more details about hyperparameter tuning (`linear  <../auto_examples/plot_gridsearch_tutorial.html>`_
-# and `deep learning  <../tutorials/Parameter_Selection_for_Neural_Networks.html>`_).
+# Because this functionality is more complex and cannot be adequately demonstrated within a code snippet, please refer to these two tutorials for more details about hyperparameter tuning (`linear  <../auto_examples/plot_linear_gridsearch_tutorial.html>`_ and `deep learning  <../tutorials/Parameter_Selection_for_Neural_Networks.html>`_).
 # Another thing to consider is that hyperparameter search can be time-consuming, especially in the case of deep learning. 
 # Users need to conduct this step with consideration of the available resources and time.
 #
@@ -214,7 +213,7 @@
 # To use as much information as possible, for linear methods, after determining the best hyperparameters, all available data are generally trained under these optimal hyperparameters to obtain the final model. 
 # We refer to this as the "retrain" strategy.
 #
-# For linear methods, the `tutorial <../auto_examples/plot_gridsearch_tutorial.html>`__ for hyperparameter search already handles retraining by default. 
+# For linear methods, the `tutorial <../auto_examples/plot_linear_gridsearch_tutorial.html>`_ for hyperparameter search already handles retraining by default. 
 # As for deep learning, since this additional step is not common in practice, we include it in the last section of this `tutorial <../tutorials/Parameter_Selection_for_Neural_Networks.html>`__.
 #
 # Step 6. Prediction

docs/neural_networks.rst

@@ -2,7 +2,7 @@ Tutorials
 =========
 
 .. toctree::
-    :maxdepth: 2
+    :maxdepth: 1
     :titlesonly:
 
     ../auto_examples/plot_multi_label

docs/tutorial.rst

@@ -1,5 +1,5 @@
-Handling Data with Many Labels with AttentionXML
-================================================
+Handling Data with Many Labels Using Neural Networks
+====================================================
 As time and space complexities grow linearly as the label size increases, it is inefficient to train models in its
 original label space. We consider adopting AttentionXML :cite:p:`RY19a` to address the issue by training
 models with a reduced space of labels.
@@ -38,7 +38,7 @@ There are 2 extra hyperparameters for AttentionXML that users need to know:
 
 Performance
 -----------
-We compared the performance between BiLSTM and AttentionXML as they have similar architectures. The datasest,
+We compared the performance between BiLSTM and AttentionXML as they have similar architectures. The dataset,
 Wiki10-31K, has 30,938 classes, which makes it hard for models to train in a one-vs-all manner.
 
 Both models were trained on an A100 Nvidia GPU. Their test results are shown below. Notice the difference