diff --git a/unstructured/metrics/evaluate.py b/unstructured/metrics/evaluate.py
index 9e43105fd0..f51141353b 100755
--- a/unstructured/metrics/evaluate.py
+++ b/unstructured/metrics/evaluate.py
@@ -432,97 +432,6 @@ def _generate_dataframes(self, rows):
         return df, agg_df
 
 
-def measure_text_extraction_accuracy(
-    output_dir: str,
-    source_dir: str,
-    output_list: Optional[List[str]] = None,
-    source_list: Optional[List[str]] = None,
-    export_dir: str = "metrics",
-    group_by: Optional[str] = None,
-    weights: Tuple[int, int, int] = (1, 1, 1),
-    visualize: bool = False,
-    output_type: str = "json",
-) -> None:
-    """
-    Loops through the list of structured output from all of `output_dir` or selected files from
-    `output_list`, and compare with gold-standard of the same file name under `source_dir` or
-    selected files from `source_list`.
-
-    Calculates text accuracy and percent missing. After looped through the whole list, write to tsv.
-    Also calculates the aggregated accuracy and percent missing.
-    """
-    TextExtractionMetricsCalculator(
-        documents_dir=output_dir,
-        ground_truths_dir=source_dir,
-        group_by=group_by,
-        weights=weights,
-        document_type=output_type,
-    ).on_files(document_paths=output_list, ground_truth_paths=source_list).calculate(
-        export_dir=export_dir, visualize_progress=visualize, display_agg_df=True
-    )
-
-
-def measure_element_type_accuracy(
-    output_dir: str,
-    source_dir: str,
-    output_list: Optional[List[str]] = None,
-    source_list: Optional[List[str]] = None,
-    export_dir: str = "metrics",
-    group_by: Optional[str] = None,
-    visualize: bool = False,
-):
-    """
-    Loops through the list of structured output from all of `output_dir` or selected files from
-    `output_list`, and compare with gold-standard of the same file name under `source_dir` or
-    selected files from `source_list`.
-
-    Calculates element type frequency accuracy and percent missing. After looped through the
-    whole list, write to tsv. Also calculates the aggregated accuracy.
-    """
-    ElementTypeMetricsCalculator(
-        documents_dir=output_dir,
-        ground_truths_dir=source_dir,
-        group_by=group_by,
-    ).on_files(document_paths=output_list, ground_truth_paths=source_list).calculate(
-        export_dir=export_dir, visualize_progress=visualize, display_agg_df=True
-    )
-
-
-def measure_table_structure_accuracy(
-    output_dir: str,
-    source_dir: str,
-    output_list: Optional[List[str]] = None,
-    source_list: Optional[List[str]] = None,
-    export_dir: str = "metrics",
-    visualize: bool = False,
-    cutoff: Optional[float] = None,
-):
-    """
-    Loops through the list of structured output from all of `output_dir` or selected files from
-    `output_list`, and compare with gold-standard of the same file name under `source_dir` or
-    selected files from `source_list`. Supports also a json file with filenames as keys and
-    structured gold-standard output as values.
-
-    Calculates:
-        - table found accuracy
-        - table level accuracy
-        - element in column index accuracy
-        - element in row index accuracy
-        - element's column content accuracy
-        - element's row content accuracy
-
-
-    After looped through the whole list, write to tsv. Also calculates the aggregated accuracy.
-    """
-    TableStructureMetricsCalculator(
-        documents_dir=output_dir,
-        ground_truths_dir=source_dir,
-        cutoff=cutoff,
-    ).on_files(document_paths=output_list, ground_truth_paths=source_list).calculate(
-        export_dir=export_dir, visualize_progress=visualize, display_agg_df=True
-    )
-
-
 def get_mean_grouping(
     group_by: str,
     data_input: Union[pd.DataFrame, str],