diff --git a/transformer_ranker/datacleaner.py b/transformer_ranker/datacleaner.py
index 388d7b0..7273c21 100644
--- a/transformer_ranker/datacleaner.py
+++ b/transformer_ranker/datacleaner.py
@@ -1,12 +1,12 @@
-import torch
+import logging
+from typing import Dict, List, Optional, Set, Tuple, Type, Union
+
 import datasets
-from datasets.dataset_dict import DatasetDict, Dataset
+import torch
+from datasets.dataset_dict import Dataset, DatasetDict
 from tokenizers.pre_tokenizers import Whitespace
-from .utils import configure_logger
-
-import logging
-from typing import List, Dict, Optional, Set, Union, Tuple, Type
 
+from .utils import configure_logger
 
 logger = configure_logger('transformer_ranker', logging.INFO)
 
@@ -189,10 +189,10 @@ def merge_texts(example: Dict[str, str]) -> Dict[str, str]:
     def _find_task_type(label_column: str, label_type: Union[Type[int], Type[str], Type[list], Type[float]]) -> str:
         """Determine task type based on the label column's data type."""
         label_type_to_task_type = {
-            int: "sentence classification",  # labels can be integers
-            str: "sentence classification",  # or strings e.g. "positive"
-            list: "word classification",
-            float: "sentence regression",
+            int: "text classification",  # labels can be integers
+            str: "text classification",  # or strings e.g. "positive"
+            list: "token classification",
+            float: "text regression",
         }
 
         task_type = label_type_to_task_type.get(label_type, None)
diff --git a/transformer_ranker/embedder.py b/transformer_ranker/embedder.py
index 8205b04..81b7a3f 100644
--- a/transformer_ranker/embedder.py
+++ b/transformer_ranker/embedder.py
@@ -1,11 +1,9 @@
-from transformers import AutoModel, AutoTokenizer
-from tokenizers.pre_tokenizers import Whitespace
-import torch
+from typing import List, Optional, Union
 
+import torch
+from tokenizers.pre_tokenizers import Whitespace
 from tqdm import tqdm
-from typing import Optional, List, Union
-
-from transformers import PreTrainedTokenizerFast
+from transformers import AutoModel, AutoTokenizer, PreTrainedTokenizerFast
 
 
 class Embedder:
diff --git a/transformer_ranker/ranker.py b/transformer_ranker/ranker.py
index a4278c9..26e4c8c 100644
--- a/transformer_ranker/ranker.py
+++ b/transformer_ranker/ranker.py
@@ -1,15 +1,15 @@
+import logging
+from typing import List, Optional, Union
+
 import torch
 from datasets.dataset_dict import Dataset, DatasetDict
 from tqdm import tqdm
 
 from .datacleaner import DatasetCleaner
 from .embedder import Embedder
-from .estimators import HScore, LogME, KNN
+from .estimators import KNN, HScore, LogME
 from .utils import Result, configure_logger
 
-import logging
-from typing import List, Optional, Union
-
 logger = configure_logger('transformer_ranker', logging.INFO)
 
 
@@ -91,7 +91,7 @@ def run(
             layer_pooling = "mean" if "mean" in layer_aggregator else None
 
             # Sentence pooling is only applied for text classification tasks
-            effective_sentence_pooling = None if self.task_type == "word classification" else sentence_pooling
+            effective_sentence_pooling = None if self.task_type == "token classification" else sentence_pooling
 
             embedder = Embedder(
                 model=model,
@@ -110,7 +110,7 @@ def run(
             )
 
             # Single list of embeddings for sequence tagging tasks
-            if self.task_type == "word classification":
+            if self.task_type == "token classification":
                 embeddings = [word_embedding for sentence_embedding in embeddings
                               for word_embedding in sentence_embedding]
 
@@ -194,15 +194,15 @@ def _confirm_ranker_setup(self, estimator, layer_aggregator) -> None:
             raise ValueError(f"Unsupported layer pooling: {layer_aggregator}. "
                              f"Use one of the following {valid_layer_aggregators}")
 
-        valid_task_types = ["sentence classification", "word classification", "sentence regression"]
+        valid_task_types = ["text classification", "token classification", "text regression"]
         if self.task_type not in valid_task_types:
             raise ValueError("Unable to determine task type of the dataset. Please specify it as a parameter: "
-                             "task_type= \"sentence classification\", \"sentence regression\", or "
-                             "\"word classification\"")
+                             "task_type= \"text classification\", \"token classification\", or "
+                             "\"text regression\"")
 
     def _estimate_score(self, estimator, embeddings: torch.Tensor, labels: torch.Tensor) -> float:
         """Use an estimator to score a transformer"""
-        regression = self.task_type == "sentence regression"
+        regression = self.task_type == "text regression"
         if estimator in ['hscore'] and regression:
             logger.warning(f'Specified estimator="{estimator}" does not support regression tasks.')
 
diff --git a/transformer_ranker/utils.py b/transformer_ranker/utils.py
index 4c0936f..b708630 100644
--- a/transformer_ranker/utils.py
+++ b/transformer_ranker/utils.py
@@ -1,9 +1,9 @@
 import logging
-import warnings
-from transformers import logging as transformers_logging
 import operator
+import warnings
+from typing import Dict, List
 
-from typing import List, Dict
+from transformers import logging as transformers_logging
 
 
 def prepare_popular_models(model_size='base') -> List[str]: