File-level Doc-string Changes for Classifiers and Presets (#604)

* Some file-level doc-string fixes

* Remove defaults from dropout values in classifier

Author: abheesht17

keras_nlp/models/bert/bert_classifier.py

@@ -11,7 +11,7 @@
 # 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.
-"""BERT task specific models and heads."""
+"""BERT classification model."""
 
 import copy
 import os
@@ -50,8 +50,8 @@ class BertClassifier(PipelineModel):
     Args:
         backbone: A `keras_nlp.models.BertBackbone` instance.
         num_classes: int. Number of classes to predict.
-        dropout: float, defaults to 0.1. The dropout probability value, applied
-            after the dense layer.
+        dropout: float. The dropout probability value, applied after the dense
+            layer.
         preprocessor: A `keras_nlp.models.BertPreprocessor` or `None`. If
             `None`, this model will not apply preprocessing, and inputs should
             be preprocessed before calling the model.

keras_nlp/models/bert/bert_classifier_test.py

@@ -11,7 +11,7 @@
 # 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.
-"""Tests for BERT task specific models and heads."""
+"""Tests for BERT classification model."""
 
 import os
 

keras_nlp/models/bert/bert_presets.py

@@ -11,6 +11,7 @@
 # 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.
+"""BERT model preset configurations."""
 
 # TODO(jbischof): document presets in keras.io and use URL in docstrings
 # Metadata for loading pretrained model weights.

keras_nlp/models/distil_bert/distil_bert_classifier.py

@@ -11,7 +11,7 @@
 # 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.
-"""DistilBERT task specific models and heads."""
+"""DistilBERT classification model."""
 
 import copy
 
@@ -53,8 +53,8 @@ class DistilBertClassifier(PipelineModel):
         backbone: A `keras_nlp.models.DistilBert` instance.
         num_classes: int. Number of classes to predict.
         hidden_dim: int. The size of the pooler layer.
-        dropout: float, defaults to 0.2. The dropout probability value, applied
-            after the first dense layer.
+        dropout: float. The dropout probability value, applied after the first
+            dense layer.
         preprocessor: A `keras_nlp.models.DistilBertPreprocessor` or `None`. If
             `None`, this model will not apply preprocessing, and inputs should
             be preprocessed before calling the model.

keras_nlp/models/distil_bert/distil_bert_classifier_test.py

@@ -11,7 +11,7 @@
 # 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.
-"""Tests for DistilBERT task specific models and heads."""
+"""Tests for DistilBERT classification model."""
 
 import os
 

keras_nlp/models/distil_bert/distil_bert_presets.py

@@ -11,6 +11,8 @@
 # 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.
+"""DistilBERT model preset configurations."""
+
 backbone_presets = {
     "distil_bert_base_en_uncased": {
         "config": {

keras_nlp/models/gpt2/gpt2_presets.py

@@ -11,6 +11,7 @@
 # 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.
+"""GPT-2 model preset configurations."""
 
 # Metadata for loading pretrained model weights.
 backbone_presets = {

keras_nlp/models/roberta/roberta_classifier.py

@@ -11,7 +11,7 @@
 # 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.
-"""RoBERTa task specific models and heads."""
+"""RoBERTa classification model."""
 
 import copy
 
@@ -49,8 +49,8 @@ class RobertaClassifier(PipelineModel):
         backbone: A `keras_nlp.models.RobertaBackbone` instance.
         num_classes: int. Number of classes to predict.
         hidden_dim: int. The size of the pooler layer.
-        dropout: float, defaults to 0.0. The dropout probability value, applied
-            to the pooled output, and after the first dense layer.
+        dropout: float. The dropout probability value, applied to the pooled
+            output, and after the first dense layer.
         preprocessor: A `keras_nlp.models.BertPreprocessor` or `None`. If
             `None`, this model will not apply preprocessing, and inputs should
             be preprocessed before calling the model.

keras_nlp/models/roberta/roberta_classifier_test.py

@@ -11,7 +11,7 @@
 # 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.
-"""Tests for RoBERTa task specific models and heads."""
+"""Tests for RoBERTa classification model."""
 
 import os
 

keras_nlp/models/roberta/roberta_presets.py

@@ -11,6 +11,8 @@
 # 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.
+"""RoBERTa model preset configurations."""
+
 backbone_presets = {
     "roberta_base": {
         "config": {