Tidy up

Author: ines

spacy_pytorch_transformers/language.py

@@ -120,7 +120,7 @@ def resume_training(self, sgd=None, component_cfg=None, **kwargs):
         for name, component in self.pipeline:
             if name == tok2vec_name:
                 continue
-            elif getattr(component, "model", None) != True:
+            elif getattr(component, "model", None) is not True:
                 continue
             elif not hasattr(component, "begin_training"):
                 continue

spacy_pytorch_transformers/model_registry.py

@@ -1,6 +1,6 @@
 from thinc.api import layerize, chain, flatten_add_lengths, with_getitem
-from thinc.t2v import Pooling, mean_pool, max_pool
-from thinc.v2v import Softmax, Maxout
+from thinc.t2v import Pooling, mean_pool
+from thinc.v2v import Softmax
 from thinc.neural.util import get_array_module
 
 
@@ -39,8 +39,7 @@ def fine_tune_class_vector(nr_class, *, exclusive_classes=True, **cfg):
     return chain(
         get_pytt_class_tokens,
         flatten_add_lengths,
-        with_getitem(0, 
-            Softmax(nr_class, cfg["token_vector_width"])),
+        with_getitem(0, Softmax(nr_class, cfg["token_vector_width"])),
         Pooling(mean_pool),
     )
 

spacy_pytorch_transformers/pipeline.py

@@ -27,5 +27,7 @@ def Model(cls, nr_class=1, exclusive_classes=False, **cfg):
         **cfg: Optional config parameters.
         RETURNS (thinc.neural.Model): The model.
         """
-        make_model = get_model_function(cfg.get("architecture", "fine_tune_class_vector"))
+        make_model = get_model_function(
+            cfg.get("architecture", "fine_tune_class_vector")
+        )
         return make_model(nr_class, exclusive_classes=exclusive_classes, **cfg)

spacy_pytorch_transformers/wrapper.py

@@ -49,7 +49,7 @@ def predict(self, ids: Array):
             y_var = self._model(ids, **model_kwargs)
         self._model.training = is_training
         return Activations.from_pytt(y_var, is_grad=False)
- 
+
     def begin_update(
         self, ids: Array, drop: Dropout = 0.0
     ) -> Tuple[Activations, Callable[..., None]]: