diff --git a/prover/datamodule.py b/prover/datamodule.py
index 63fec20..2893c20 100644
--- a/prover/datamodule.py
+++ b/prover/datamodule.py
@@ -138,7 +138,9 @@ def __init__(
     ) -> None:
         super().__init__()
         max_len = max(max_input_len, max_output_len)
-        self.tokenizer = AutoTokenizer.from_pretrained(model_name, model_max_length=max_len)
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            model_name, model_max_length=max_len
+        )
         self.max_input_len = max_input_len
         self.max_output_len = max_output_len
         self.is_train = is_train
@@ -213,7 +215,9 @@ def __init__(
     ) -> None:
         super().__init__()
         max_len = max(max_input_len, max_output_len)
-        self.tokenizer = AutoTokenizer.from_pretrained(model_name, model_max_length=max_len)
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            model_name, model_max_length=max_len
+        )
         self.max_input_len = max_input_len
         self.max_output_len = max_output_len
         self.sample_goal = sample_goal
diff --git a/prover/model.py b/prover/model.py
index 44b014a..ca99861 100644
--- a/prover/model.py
+++ b/prover/model.py
@@ -136,7 +136,9 @@ def __init__(
                 EntailmentClassifier.load_from_checkpoint(verifier_ckpt)
             ]  # Avoid making the verifier a submodule.
 
-        self.tokenizer = AutoTokenizer.from_pretrained(model_name, model_max_length=max_input_len)
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            model_name, model_max_length=max_input_len
+        )
         if (
             model_name.startswith("t5-")
             or model_name.startswith("google/t5-v1_1-")
@@ -680,7 +682,7 @@ def configure_optimizers(self) -> Dict[str, Any]:
         assert self.trainer is not None
         if self.trainer.max_steps != -1:
             max_steps = self.trainer.max_steps
-        else: 
+        else:
             max_steps = (
                 self.trainer.max_epochs
                 * len(self.trainer.datamodule.train_dataloader())  # type: ignore
diff --git a/verifier/datamodule.py b/verifier/datamodule.py
index a17f8c4..f9debba 100644
--- a/verifier/datamodule.py
+++ b/verifier/datamodule.py
@@ -64,7 +64,9 @@ def __init__(
         irrelevant_distractors_only: bool,
     ) -> None:
         super().__init__()
-        self.tokenizer = AutoTokenizer.from_pretrained(model_name, model_max_length=max_input_len)
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            model_name, model_max_length=max_input_len
+        )
         assert split in ("train", "val")
         self.split = split
         self.max_num_premises = max_num_premises  # The maximum number of premises used in data augmentation.
diff --git a/verifier/model.py b/verifier/model.py
index 8b4ea2a..2486b53 100644
--- a/verifier/model.py
+++ b/verifier/model.py
@@ -6,7 +6,14 @@
 import torch.nn.functional as F
 import pytorch_lightning as pl
 from transformers import AutoTokenizer, AutoModel
-from torchmetrics.classification import BinaryAccuracy, BinaryAveragePrecision, BinaryF1Score, BinarySpecificity, BinaryRecall, BinaryPrecision
+from torchmetrics.classification import (
+    BinaryAccuracy,
+    BinaryAveragePrecision,
+    BinaryF1Score,
+    BinarySpecificity,
+    BinaryRecall,
+    BinaryPrecision,
+)
 
 
 class EntailmentClassifier(pl.LightningModule):
@@ -24,7 +31,9 @@ def __init__(
         self.warmup_steps = warmup_steps
         self.pos_weight = pos_weight
         self.max_input_len = max_input_len
-        self.tokenizer = AutoTokenizer.from_pretrained(model_name, model_max_length=max_input_len)
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            model_name, model_max_length=max_input_len
+        )
         self.encoder = AutoModel.from_pretrained(model_name)
         self.fc = nn.Linear(self.encoder.config.hidden_size, 1)
         self.metrics = {
@@ -87,7 +96,7 @@ def configure_optimizers(self) -> Dict[str, Any]:
         assert self.trainer is not None
         if self.trainer.max_steps != -1:
             max_steps = self.trainer.max_steps
-        else: 
+        else:
             max_steps = (
                 self.trainer.max_epochs
                 * len(self.trainer.datamodule.train_dataloader())  # type: ignore