docs: document ScoreFilter benchmark metrics for 26.04

fern/versions/v26.04/pages/about/release-notes/index.mdx

@@ -53,6 +53,10 @@ Upgraded Cosmos-Xenna from 0.1.2 to 0.2.0 with a simplified resource model and i
 - **Xenna-managed CUDA devices**: Xenna now manages CUDA device visibility directly, replacing the previous Ray-managed approach.
 - **Ray 2.54**: Updated Ray dependency to version 2.54 for compatibility with Cosmos-Xenna 0.2.0.
 
+### Pipeline Stage Metrics (PR #1385)
+
+Pipeline stages now track document-level metrics through `StagePerfStats.num_items_processed`, so you can see how each stage affects your dataset. After calling `pipeline.run()`, the returned task objects expose per-stage document counts that you can use to monitor filtering behavior and tune thresholds.
+
 ### Workflow Results API
 
 Standardized return type for all deduplication workflows:

fern/versions/v26.04/pages/curate-text/process-data/quality-assessment/heuristic.mdx

@@ -487,4 +487,28 @@ reader = JsonlReader(
 
 </Tabs>
 
+## Pipeline Metrics
+
+When you run a filtering pipeline, each stage tracks the number of documents it processes. You can use these metrics to understand how each filter affects your dataset and to tune thresholds.
+
+After calling `pipeline.run()`, the returned task objects contain per-stage performance statistics through `_stage_perf`. Each entry is a `StagePerfStats` object with a `num_items_processed` field that records how many documents passed through that stage.
+
+```python
+# Run the pipeline and inspect filter metrics
+output_tasks = pipeline.run()
+
+for task in output_tasks:
+    # _stage_perf[0] is file partitioning, _stage_perf[1] is the reader
+    num_input = task._stage_perf[1].num_items_processed
+    # The last stage is the writer — its count reflects documents that survived all filters
+    num_output = task._stage_perf[-1].num_items_processed
+
+    if num_input > 0:
+        print(f"Task {task.task_id}: {num_input} input → {num_output} kept ({num_output / num_input:.1%})")
+    else:
+        print(f"Task {task.task_id}: 0 input → 0 kept")
+```
+
+These same metrics power the nightly benchmarks, which track `num_documents_processed`, `num_kept_documents`, and `throughput_docs_per_sec` for every pipeline run.
+
 Remember that the goal of filtering is to improve the quality of your training data, not necessarily to remove as many documents as possible. Monitor your filtering results and adjust thresholds based on your specific data characteristics and downstream tasks.