Fix Sphinx python docstring error: text contrib module (#12949) (#13149)

python/mxnet/contrib/text/embedding.py

@@ -161,7 +161,7 @@ class _TokenEmbedding(vocab.Vocabulary):
     pre-trained token embedding file, are taken as the indexed tokens of the embedding.
 
 
-    Properties
+    Attributes
     ----------
     token_to_idx : dict mapping str to int
         A dict mapping each token to its index integer.
@@ -506,25 +506,6 @@ class GloVe(_TokenEmbedding):
         embedding vectors, such as loaded from a pre-trained token embedding file. If None, all the
         tokens from the loaded embedding vectors, such as loaded from a pre-trained token embedding
         file, will be indexed.
-
-
-    Properties
-    ----------
-    token_to_idx : dict mapping str to int
-        A dict mapping each token to its index integer.
-    idx_to_token : list of strs
-        A list of indexed tokens where the list indices and the token indices are aligned.
-    unknown_token : hashable object
-        The representation for any unknown token. In other words, any unknown token will be indexed
-        as the same representation.
-    reserved_tokens : list of strs or None
-        A list of reserved tokens that will always be indexed.
-    vec_len : int
-        The length of the embedding vector for each token.
-    idx_to_vec : mxnet.ndarray.NDArray
-        For all the indexed tokens in this embedding, this NDArray maps each token's index to an
-        embedding vector. The largest valid index maps to the initialized embedding vector for every
-        reserved token, such as an unknown_token token and a padding token.
     """
 
     # Map a pre-trained token embedding archive file and its SHA-1 hash.
@@ -610,25 +591,6 @@ class FastText(_TokenEmbedding):
         embedding vectors, such as loaded from a pre-trained token embedding file. If None, all the
         tokens from the loaded embedding vectors, such as loaded from a pre-trained token embedding
         file, will be indexed.
-
-
-    Properties
-    ----------
-    token_to_idx : dict mapping str to int
-        A dict mapping each token to its index integer.
-    idx_to_token : list of strs
-        A list of indexed tokens where the list indices and the token indices are aligned.
-    unknown_token : hashable object
-        The representation for any unknown token. In other words, any unknown token will be indexed
-        as the same representation.
-    reserved_tokens : list of strs or None
-        A list of reserved tokens that will always be indexed.
-    vec_len : int
-        The length of the embedding vector for each token.
-    idx_to_vec : mxnet.ndarray.NDArray
-        For all the indexed tokens in this embedding, this NDArray maps each token's index to an
-        embedding vector. The largest valid index maps to the initialized embedding vector for every
-        reserved token, such as an unknown_token token and a padding token.
     """
 
     # Map a pre-trained token embedding archive file and its SHA-1 hash.
@@ -687,25 +649,6 @@ class CustomEmbedding(_TokenEmbedding):
         embedding vectors, such as loaded from a pre-trained token embedding file. If None, all the
         tokens from the loaded embedding vectors, such as loaded from a pre-trained token embedding
         file, will be indexed.
-
-
-    Properties
-    ----------
-    token_to_idx : dict mapping str to int
-        A dict mapping each token to its index integer.
-    idx_to_token : list of strs
-        A list of indexed tokens where the list indices and the token indices are aligned.
-    unknown_token : hashable object
-        The representation for any unknown token. In other words, any unknown token will be indexed
-        as the same representation.
-    reserved_tokens : list of strs or None
-        A list of reserved tokens that will always be indexed.
-    vec_len : int
-        The length of the embedding vector for each token.
-    idx_to_vec : mxnet.ndarray.NDArray
-        For all the indexed tokens in this embedding, this NDArray maps each token's index to an
-        embedding vector. The largest valid index maps to the initialized embedding vector for every
-        reserved token, such as an unknown_token token and a padding token.
     """
 
     def __init__(self, pretrained_file_path, elem_delim=' ', encoding='utf8',
@@ -735,25 +678,6 @@ class CompositeEmbedding(_TokenEmbedding):
     token_embeddings : instance or list of `mxnet.contrib.text.embedding._TokenEmbedding`
         One or multiple pre-trained token embeddings to load. If it is a list of multiple
         embeddings, these embedding vectors will be concatenated for each token.
-
-
-    Properties
-    ----------
-    token_to_idx : dict mapping str to int
-        A dict mapping each token to its index integer.
-    idx_to_token : list of strs
-        A list of indexed tokens where the list indices and the token indices are aligned.
-    unknown_token : hashable object
-        The representation for any unknown token. In other words, any unknown token will be indexed
-        as the same representation.
-    reserved_tokens : list of strs or None
-        A list of reserved tokens that will always be indexed.
-    vec_len : int
-        The length of the embedding vector for each token.
-    idx_to_vec : mxnet.ndarray.NDArray
-        For all the indexed tokens in this embedding, this NDArray maps each token's index to an
-        embedding vector. The largest valid index maps to the initialized embedding vector for every
-        reserved token, such as an unknown_token token and a padding token.
     """
     def __init__(self, vocabulary, token_embeddings):
 

python/mxnet/contrib/text/vocab.py

@@ -63,12 +63,8 @@ class Vocabulary(object):
         `reserved_tokens` must be of the same hashable type. Examples: str, int, and tuple.
 
 
-    Properties
+    Attributes
     ----------
-    token_to_idx : dict mapping str to int
-        A dict mapping each token to its index integer.
-    idx_to_token : list of strs
-        A list of indexed tokens where the list indices and the token indices are aligned.
     unknown_token : hashable object
         The representation for any unknown token. In other words, any unknown token will be indexed
         as the same representation.
@@ -143,10 +139,16 @@ def __len__(self):
 
     @property
     def token_to_idx(self):
+        """
+        dict mapping str to int: A dict mapping each token to its index integer.
+        """
         return self._token_to_idx
 
     @property
     def idx_to_token(self):
+        """
+        list of strs:  A list of indexed tokens where the list indices and the token indices are aligned.
+        """
         return self._idx_to_token
 
     @property