Add show_progress_bar parameter

README.md

@@ -56,6 +56,7 @@ Args:
 - `random_seed` (_int, optional_): Random seed for Python and PyTorch. Default is `0`.
 - `inference_batch_size` (_int, optional_): Batch size for inference. Default is `1`.
 - `inference_mask_evenly` (_bool, optional_): Whether to mask every `gap` tokens during inference (`True`) or mask randomly with a probability of 0.15 (`False`). Default is `True`.
+- `show_progress_bar` (_bool, optional_): Whether to display a progress bar during computation. Default is `True`.
 
 BLANC-help specific arguments:
 - `filler_token` (_str, optional_): Token to use as filler in lieu of the summary. Default is `.`.

blanc_score.py

@@ -37,7 +37,8 @@ Args:
     random_seed (int, optional): Random seed for Python and PyTorch. Default is 0.
     inference_batch_size (int, optional): Batch size for inference. Default is 1.
     inference_mask_evenly (bool, optional): Whether to mask every `gap` tokens during inference (True) or mask randomly with a probability of 0.15 (False). Default is True.
-
+    show_progress_bar (bool, optional): Whether to show progress bars during evaluation. Default is True.
+    
     BLANC-help specific arguments:
         filler_token (str, optional): Token to use as filler in lieu of the summary. Default is ".".
         help_sep (str, optional): Token used to separate the summary (or filler) from the sentence, or '' for no separator. Default is "".
@@ -132,6 +133,7 @@ class BLANC(evaluate.Metric):
             finetune_chunk_stride=32,
             learning_rate=5e-05,
             warmup_steps=0,
+            show_progress_bar=True,
     ):
         # Choose between BLANC-help and BLANC-tune methods based on measure argument.
         if blanc_score == "help":
@@ -152,6 +154,7 @@ class BLANC(evaluate.Metric):
                 inference_mask_evenly=inference_mask_evenly,
                 filler_token=filler_token,
                 help_sep=help_sep,
+                show_progress_bar=show_progress_bar,
             )
         elif blanc_score == "tune":
             blanc_instance = BlancTune(
@@ -177,6 +180,7 @@ class BLANC(evaluate.Metric):
                 finetune_chunk_stride=finetune_chunk_stride,
                 learning_rate=learning_rate,
                 warmup_steps=warmup_steps,
+                show_progress_bar=show_progress_bar,
             )
         else:
             raise ValueError(f"Invalid measure argument: {measure}. Choose 'help' or 'tune'.")