diff --git a/src/instructlab/sdg/eval_data.py b/src/instructlab/sdg/eval_data.py
index 4afcd5ff..a95bd597 100644
--- a/src/instructlab/sdg/eval_data.py
+++ b/src/instructlab/sdg/eval_data.py
@@ -55,7 +55,11 @@ def _format_mmlu_style(ds: Dataset) -> Dataset:
     ds = ds.filter(lambda x: x["choices"])
     ds = ds.filter(lambda x: len(x["choices"]) == 4)
     ds = ds.filter(lambda x: x["answer"] in ["A", "B", "C", "D"])
-    ds = ds.class_encode_column("answer")
+    # We filter out a lot of the dataset above (and in _post_process_mcq)
+    # if we've managed to filter out all of the results we don't want to run class_encode_column
+    # as the answer column might not exist
+    if len(ds):
+        ds = ds.class_encode_column("answer")
     return ds
 
 
@@ -113,7 +117,8 @@ def generate_eval_task_data(
     mmlubench_pipe, task_name, samples, output_dir, date_suffix
 ):
     mmlubench_data = mmlubench_pipe.generate(samples)
-    mmlubench_data = _post_process_mcq(mmlubench_data)
+    if len(mmlubench_data):
+        mmlubench_data = _post_process_mcq(mmlubench_data)
 
     eval_data_file_path = (
         f"{output_dir}/node_datasets_{date_suffix}/mmlubench_{task_name}.jsonl"
diff --git a/src/instructlab/sdg/generate_data.py b/src/instructlab/sdg/generate_data.py
index 80c80944..4d6fffe3 100644
--- a/src/instructlab/sdg/generate_data.py
+++ b/src/instructlab/sdg/generate_data.py
@@ -24,6 +24,7 @@
 from instructlab.sdg.pipeline import (
     FULL_PIPELINES_PACKAGE,
     SIMPLE_PIPELINES_PACKAGE,
+    EmptyDatasetError,
     Pipeline,
     PipelineContext,
 )
@@ -371,6 +372,10 @@ def generate_data(
         ds = Dataset.from_list(samples)
         logger.debug("Dataset: %s" % ds)
         new_generated_data = sdg.generate(ds)
+        if len(new_generated_data) == 0:
+            raise EmptyDatasetError(
+                "Pipeline stopped: Empty dataset after running pipe"
+            )
         generated_data = (
             [new_generated_data]
             if generated_data is None
@@ -384,7 +389,7 @@ def generate_data(
             generate_eval_task_data(
                 mmlu_bench_pipe,
                 leaf_node_path,
-                new_generated_data,
+                ds,
                 output_dir,
                 date_suffix,
             )
diff --git a/src/instructlab/sdg/pipeline.py b/src/instructlab/sdg/pipeline.py
index ae71c2af..1263c974 100644
--- a/src/instructlab/sdg/pipeline.py
+++ b/src/instructlab/sdg/pipeline.py
@@ -184,9 +184,7 @@ def _generate_single(self, dataset) -> Dataset:
 
             # If at any point we end up with an empty data set, the pipeline has failed
             if len(dataset) == 0:
-                raise EmptyDatasetError(
-                    f"Pipeline stopped: Empty dataset after running block: {block_name}"
-                )
+                return dataset
 
             drop_columns_in_ds = [e for e in drop_columns if e in dataset.column_names]
             if drop_columns: