docs: minor changes

deeppavlov/core/trainers/nn_trainer.py

@@ -52,7 +52,7 @@ class NNTrainer(FitTrainer):
         train_metrics: metrics calculated for train logs (if omitted, ``metrics`` argument is used)
         metric_optimization: one of ``'maximize'`` or ``'minimize'`` — strategy for metric optimization used in early
             stopping (default is ``'maximize'``)
-        evaluation_targets: data types on which to evaluate trained pipeline (default is ``('valid', 'test')``)
+        evaluation_targets: data types on which to evaluate a trained pipeline (default is ``('valid', 'test')``)
         show_examples: a flag used to print inputs, expected outputs and predicted outputs for the last batch
             in evaluation logs (default is ``False``)
         tensorboard_log_dir: path to a directory where tensorboard logs can be stored, ignored if None
@@ -75,7 +75,7 @@ class NNTrainer(FitTrainer):
         **kwargs: additional parameters whose names will be logged but otherwise ignored
 
 
-    Trainer saves the model if it sees a progress in scores. The full rules look like following:
+    Trainer saves the model if it sees progress in scores. The full rules look like following:
 
     - For the validation savepoint:
         * 0-th validation (optional). Don't save model, establish a baseline.
@@ -143,7 +143,7 @@ def _improved(op):
         self.patience = 0
         self.last_result = {}
         self.losses = []
-        self.start_time = None  # type:Optional[float]
+        self.start_time: Optional[float] = None
 
         if self.tensorboard_log_dir is not None:
             self.tb_train_writer = self._tf.summary.FileWriter(str(self.tensorboard_log_dir / 'train_log'))
@@ -208,7 +208,7 @@ def _validate(self, iterator: DataLearningIterator,
             log.info('Saving model')
             self.save()
         else:
-            log.info('Did not improved on the {} of {}'.format(m_name, self.score_best))
+            log.info('Did not improve on the {} of {}'.format(m_name, self.score_best))
 
         report['impatience'] = self.patience
         if self.validation_patience > 0:

deeppavlov/dataset_readers/file_paths_reader.py

@@ -57,7 +57,7 @@ def _get_files(self, data_path, tgt):
             paths = Path(data_path).resolve().glob(tgt)
             files = [file for file in paths if Path(file).is_file()]
             paths_info = Path(data_path, tgt).absolute().as_posix()
-            if not (files):
+            if not files:
                 raise Exception(f"Not find files. Data path '{paths_info}' does not exist or does not hold files!")
             else:
                 log.info(f"Found {len(files)} files located '{paths_info}'.")

docs/index.rst

@@ -66,7 +66,7 @@ Welcome to DeepPavlov's documentation!
    Amazon Alexa integration <integrations/amazon_alexa>
    Microsoft Bot Framework integration <integrations/ms_bot>
    Amazon AWS deployment <integrations/aws_ec2>
-   Deeppavlov settings <integrations/settings>
+   DeepPavlov settings <integrations/settings>
 
 
 .. toctree::