diff --git a/hemm/eval_pipelines/__init__.py b/hemm/eval_pipelines/__init__.py
deleted file mode 100644
index dea3c26..0000000
--- a/hemm/eval_pipelines/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .eval_pipeline import EvaluationPipeline
-
-__all__ = ["EvaluationPipeline"]
diff --git a/hemm/eval_pipelines/eval_pipeline.py b/hemm/eval_pipelines/eval_pipeline.py
deleted file mode 100644
index d031b49..0000000
--- a/hemm/eval_pipelines/eval_pipeline.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import asyncio
-from abc import ABC
-from typing import Dict, List, Union
-
-import weave
-
-
-class EvaluationPipeline(ABC):
-    """Evaluation pipeline to evaluate the a multi-modal generative model.
-
-    Args:
-        model (BaseDiffusionModel): The model to evaluate.
-    """
-
-    def __init__(self, model: weave.Model) -> None:
-        super().__init__()
-        self.model = model
-
-        self.image_size = (self.model.image_height, self.model.image_width)
-        self.scorers = []
-
-    def add_metric(self, metric: Union[callable, weave.Scorer]):
-        """Add a metric function to the evaluation pipeline.
-
-        Args:
-            metric (BaseMetric): Metric function to evaluate the generated images.
-        """
-        self.scorers.append(metric)
-
-    def __call__(self, dataset: Union[List[Dict], str]) -> Dict[str, float]:
-        """Evaluate the Stable Diffusion model on the given dataset.
-
-        Args:
-            dataset (Union[List[Dict], str]): Dataset to evaluate the model on. If a string is
-                passed, it is assumed to be a Weave dataset reference.
-        """
-        dataset = weave.ref(dataset).get() if isinstance(dataset, str) else dataset
-        evaluation = weave.Evaluation(dataset=dataset, scorers=self.scorers)
-        summary = asyncio.run(evaluation.evaluate())
-        return summary