remove unnecesory data

dicrect_compute_metric.py

@@ -1,49 +0,0 @@
-from typing import Optional
-
-import evaluate
-
-
-class DirectComputeMetric(evaluate.EvaluationModule):
-    """
-    Base class for metrics that directly compute the score from the predictions and references without add_batch
-    """
-
-    def compute(self, *, predictions=None, references=None, **kwargs) -> Optional[dict]:
-        """Compute the evaluation module.
-
-        Usage of positional arguments is not allowed to prevent mistakes.
-
-        Args:
-            predictions (`list/array/tensor`, *optional*):
-                Predictions.
-            references (`list/array/tensor`, *optional*):
-                References.
-            **kwargs (optional):
-                Keyword arguments that will be forwarded to the evaluation module [`~evaluate.EvaluationModule.compute`]
-                method (see details in the docstring).
-
-        Return:
-            `dict` or `None`
-
-            - Dictionary with the results if this evaluation module is run on the main process (`process_id == 0`).
-            - `None` if the evaluation module is not run on the main process (`process_id != 0`).
-
-        ```py
-        >>> import evaluate
-        >>> accuracy =  evaluate.load("accuracy")
-        >>> accuracy.compute(predictions=[0, 1, 1, 0], references=[0, 1, 0, 1])
-        ```
-        """
-        all_kwargs = {"predictions": predictions, "references": references, **kwargs}
-        if predictions is None and references is None:
-            missing_kwargs = {k: None for k in self._feature_names() if k not in all_kwargs}
-            all_kwargs.update(missing_kwargs)
-        else:
-            missing_inputs = [k for k in self._feature_names() if k not in all_kwargs]
-            if missing_inputs:
-                raise ValueError(
-                    f"Evaluation module inputs are missing: {missing_inputs}. All required inputs are {list(self._feature_names())}"
-                )
-        inputs = {input_name: all_kwargs[input_name] for input_name in self._feature_names()}
-        compute_kwargs = {k: kwargs[k] for k in kwargs if k not in self._feature_names()}
-        return self._compute(**inputs, **compute_kwargs)