Tokenizer and config

src/transformers/configuration_deberta.py

@@ -12,24 +12,29 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-""" DeBERTa model configuration """
+""" DeDeBERTaa model configuration """
 
 from .configuration_utils import PretrainedConfig
 from .utils import logging
 
 
 logger = logging.get_logger(__name__)
 
-DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+DEDeBERTaA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
     "microsoft/deberta-base": "https://s3.amazonaws.com/models.huggingface.co/bert/microsoft/deberta-base/config.json",
     "microsoft/deberta-large": "https://s3.amazonaws.com/models.huggingface.co/bert/microsoft/deberta-large/config.json",
 }
 
 
 class DebertaConfig(PretrainedConfig):
     r"""
-    :class:`~transformers.DebertaConfig` is the configuration class to store the configuration of a
-    :class:`~transformers.DebertaModel`.
+    This is the configuration class to store the configuration of a :class:`~transformers.DebertaModel` or a
+    :class:`~transformers.TFDebertaModel`. It is used to instantiate a DeBERTa model according to the specified
+    arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar
+    configuration to that of the DeBERTa `bert-base-uncased <https://huggingface.co/bert-base-uncased>`__ architecture.
+
+    Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
+    outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
 
     Arguments:
         vocab_size (:obj:`int`, `optional`, defaults to 30522):

src/transformers/tokenization_deberta.py

@@ -581,7 +581,7 @@ def convert_tokens_to_string(self, tokens):
     def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
         """
         Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
-        adding special tokens. A BERT sequence has the following format:
+        adding special tokens. A DeBERTa sequence has the following format:
 
         - single sequence: [CLS] X [SEP]
         - pair of sequences: [CLS] A [SEP] B [SEP]
@@ -608,14 +608,15 @@ def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_spe
         special tokens using the tokenizer ``prepare_for_model`` or ``encode_plus`` methods.
 
         Args:
-            token_ids_0: list of ids (must not contain special tokens)
-            token_ids_1: Optional list of ids (must not contain special tokens), necessary when fetching sequence ids
-                for sequence pairs
-            already_has_special_tokens: (default False) Set to True if the token list is already formated with
-                special tokens for the model
+            token_ids_0 (:obj:`List[int]`):
+                List of IDs.
+            token_ids_1 (:obj:`List[int]`, `optional`):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
+                Whether or not the token list is already formatted with special tokens for the model.
 
         Returns:
-            A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+            :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
         """
 
         if already_has_special_tokens: