[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

lai_tldr/data.py

@@ -19,7 +19,6 @@
 # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
-
 """Code in this file is based on https://github.com/Shivanandroy/simpleT5 by Shivanand Roy."""
 
 import os
@@ -55,11 +54,11 @@ def __init__(
         self.target_max_token_len = target_max_token_len
 
     def __len__(self):
-        """returns length of data."""
+        """Returns length of data."""
         return len(self.data)
 
     def __getitem__(self, index: int):
-        """returns dictionary of input tensors to feed into T5/MT5 model."""
+        """Returns dictionary of input tensors to feed into T5/MT5 model."""
 
         data_row = self.data.iloc[index]
         source_text = data_row["source_text"]
@@ -85,9 +84,9 @@ def __getitem__(self, index: int):
         )
 
         labels = target_text_encoding["input_ids"]
-        labels[
-            labels == 0
-        ] = -100  # to make sure we have correct labels for T5 text generation
+        labels[labels == 0] = (
+            -100
+        )  # to make sure we have correct labels for T5 text generation
 
         return dict(
             source_text_input_ids=source_text_encoding["input_ids"].flatten(),
@@ -170,7 +169,7 @@ def setup(self, stage=None):
         )
 
     def train_dataloader(self):
-        """training dataloader."""
+        """Training dataloader."""
         return DataLoader(
             self.train_dataset,
             batch_size=self.batch_size,
@@ -179,7 +178,7 @@ def train_dataloader(self):
         )
 
     def test_dataloader(self):
-        """test dataloader."""
+        """Test dataloader."""
         return DataLoader(
             self.test_dataset,
             batch_size=self.batch_size,
@@ -188,7 +187,7 @@ def test_dataloader(self):
         )
 
     def val_dataloader(self):
-        """validation dataloader."""
+        """Validation dataloader."""
         return DataLoader(
             self.val_dataset,
             batch_size=self.batch_size,

lai_tldr/module.py

@@ -19,7 +19,6 @@
 # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
-
 """Code in this file is based on https://github.com/Shivanandroy/simpleT5 by Shivanand Roy."""
 
 from lightning.pytorch import LightningModule
@@ -42,7 +41,7 @@ def __init__(
         self.save_only_last_epoch = False
 
     def forward(self, input_ids, attention_mask, decoder_attention_mask, labels=None):
-        """forward step."""
+        """Forward step."""
         output = self.model(
             input_ids,
             attention_mask=attention_mask,
@@ -53,7 +52,7 @@ def forward(self, input_ids, attention_mask, decoder_attention_mask, labels=None
         return output.loss, output.logits
 
     def training_step(self, batch, batch_idx):
-        """training step."""
+        """Training step."""
         input_ids = batch["source_text_input_ids"]
         attention_mask = batch["source_text_attention_mask"]
         labels = batch["labels"]
@@ -70,7 +69,7 @@ def training_step(self, batch, batch_idx):
         return loss
 
     def validation_step(self, batch, batch_idx):
-        """validation step."""
+        """Validation step."""
         input_ids = batch["source_text_input_ids"]
         attention_mask = batch["source_text_attention_mask"]
         labels = batch["labels"]
@@ -86,7 +85,7 @@ def validation_step(self, batch, batch_idx):
         self.log("val_loss", loss, prog_bar=True)
 
     def test_step(self, batch, batch_idx):
-        """test step."""
+        """Test step."""
         input_ids = batch["source_text_input_ids"]
         attention_mask = batch["source_text_attention_mask"]
         labels = batch["labels"]
@@ -102,7 +101,7 @@ def test_step(self, batch, batch_idx):
         self.log("test_loss", loss, prog_bar=True)
 
     def configure_optimizers(self):
-        """configure optimizers."""
+        """Configure optimizers."""
         return AdamW(self.parameters(), lr=0.0001)