fix document formation bugs.

python/paddle/trainer_config_helpers/evaluators.py

@@ -350,20 +350,23 @@ def chunk_evaluator(
     sequence. It calculates precision, recall and F1 scores for the chunk detection.
 
     To use chunk evaluator, several concepts need to be clarified firstly.
-    Chunk type is the type of the whole chunk and a chunk consists of one or several words.  (For example in NER, ORG for organization name, PER for person name etc.)
-    Tag indicates the position of a word in a chunk. (B for begin, I for inside, E for end, S for single)
+
+    * **Chunk type** is the type of the whole chunk and a chunk consists of one or several words.  (For example in NER, ORG for organization name, PER for person name etc.)
+
+    * **Tag type** indicates the position of a word in a chunk. (B for begin, I for inside, E for end, S for single)
     We can name a label by combining tag type and chunk type. (ie. B-ORG for begining of an organization name)
 
-    The construction of label dict should obey the following rules:
-    (1) Use one of the listed labelling schemes. These schemes differ in ways indicating chunk boundry.
+    The construction of label dictionary should obey the following rules:
+
+    - Use one of the listed labelling schemes. These schemes differ in ways indicating chunk boundry.
+
+    .. code-block:: text
 
-    .. code-block:: python
         Scheme    Description                                                                                  
         plain    Use the same label for the whole chunk.
         IOB      Two labels for chunk type X, B-X for chunk begining and I-X for chunk inside. 
         IOE      Two labels for chunk type X, E-X for chunk ending and I-X for chunk inside.
         IOBES    Four labels for chunk type X, B-X for chunk begining, I-X for chunk inside, E-X for chunk end and S-X for single word chunk. 
-    .. code-block:: python
    
     To make it clear, let's illustrate by an NER example.
     Assuming that there are three named entity types including ORG, PER and LOC which are called 'chunk type' here,
@@ -372,29 +375,30 @@ def chunk_evaluator(
     Prefixes which are called 'tag type' here are added to chunk types and there are two tag types including B and I.
     Of course, the training data should be labeled accordingly.
 
-    (2) Mapping is done correctly by the listed equations and assigning protocol.
+    - Mapping is done correctly by the listed equations and assigning protocol.
 
     The following table are equations to extract tag type and chunk type from a label.
 
-    .. code-block:: python
+    .. code-block:: text
+
         tagType = label % numTagType
         chunkType = label / numTagType
         otherChunkType = numChunkTypes
-    .. code-block:: python
     
     The following table shows the mapping rule between tagType and tag type in each scheme.
 
-    .. code-block:: python
+    .. code-block:: text
+
         Scheme Begin Inside End   Single
         plain  0     -      -     -
         IOB    0     1      -     -
         IOE    -     0      1     -
         IOBES  0     1      2     3
-    .. code-block:: python
 
     Continue the NER example, and the label dict should look like this to satify above equations:
 
-    .. code-block:: python
+    .. code-block:: text
+
         B-ORG  0
         I-ORG  1
         B-PER  2
@@ -402,12 +406,11 @@ def chunk_evaluator(
         B-LOC  4
         I-LOC  5
         O      6
-    .. code-block:: python
 
     In this example, chunkType has three values: 0 for ORG, 1 for PER, 2 for LOC, because the scheme is
     "IOB" so tagType has two values: 0 for B and 1 for I. 
     Here we will use I-LOC to explain the above mapping rules in detail.
-    For I-LOC, the label id is 5, so we can get tagType=1 and ChunkType=2, which means I-LOC is a part of NER chunk LOC
+    For I-LOC, the label id is 5, so we can get tagType=1 and chunkType=2, which means I-LOC is a part of NER chunk LOC
     and the tag is I.
 
     The simple usage is:
@@ -416,7 +419,6 @@ def chunk_evaluator(
 
        eval = chunk_evaluator(input, label, chunk_scheme, num_chunk_types)
 
-    .. code-block:: python
     
     :param input: The input layers.
     :type input: LayerOutput