Update ettin-150m-memory-reranker-ft-v1 documentation

README.md

@@ -129,44 +129,44 @@ Both models rerank the same 50 candidates that upstream Gemma retrieved for each
 
 These are nDCG@10 scores for reranking fixed candidate pools, not dense Gemma scores. Fine-tuning lowered FiQA and slightly raised SciFact. Later continuation recipes did not recover FiQA while keeping Daecore ranking quality, so the original learned weights remain.
 
-### Full pipeline: Gemma v2 + BM25 + Ettin
+### Full pipeline: the effect of fine-tuning Ettin
 
-This replay measures semantic and lexical search, reciprocal-rank fusion, then
-Ettin reranking over **82,719 passages**. The table uses the same **925 queries
-with known useful evidence** as Gemma's dense comparison, selected from the
-970-query source panel. A query remains included when this pipeline misses its
-answer. Both cards share this CUDA FP16 table; it is not either model's
-standalone score.
+Only the reranker changes: **upstream Ettin → Daecore Ettin**. Gemma v2,
+BM25 and reciprocal-rank fusion supply the same 50 candidates from
+**82,719 passages**. Each cell reads before → **after**, isolating what
+fine-tuning adds to the ordering of the actual hybrid candidates.
 
-**Reference:** 122,231 graded query–passage pairs for these 925 queries, within
-the shared 127,665-pair top-50 reference (September 2026). Abstentions are excluded.
+**Reference:** the same **925 known-answerable queries**, frozen before this
+comparison · **122,783 graded query–passage pairs** · October 2026. Retrieval
+misses remain included. Both cards share the same fully fine-tuned endpoint.
 
 | Return depth | Hit | Precision | Known-positive recall | nDCG |
 |---|---:|---:|---:|---:|
-| 1 | 75.87% | 75.87% | 2.12% | 0.6513 |
-| 3 | 89.51% | 75.07% | 5.89% | 0.6423 |
-| 5 | 92.76% | 73.71% | 9.35% | 0.6376 |
-| 10 | 94.27% | 70.46% | 17.04% | 0.6378 |
-| 20 | 95.46% | 64.81% | 29.72% | 0.6491 |
-| 50 | 97.62% | 42.40% | 45.98% | 0.6130 |
-
-**The hybrid candidate ceiling is 97.62% Hit@50 and 45.98% known-positive
-recall@50** on this shared answerable subset. These coverage measures describe
-the actual 50-candidate pool; reranking cannot change them. Daecore returns a
-selected prefix of 3–20, so fixed-depth scores do not evaluate the selector's
-choice.
-
-Grades 2–3 count as useful. At depth 1, both providers use the same **920
-queries**; five are omitted because a first result was ungradable. Depths
-3–50 use all 925. Precision pools judged positions. Abstentions remove 52
-positions at rank 20 and 93 at rank 50 per provider, without drawing in deeper
-passages. Known-positive recall counts judged passages, not every useful
-passage in the corpus.
-
-The [evaluation companion](evaluation/README.md) retains
-coverage across all 970 source queries and explains the shared reference,
-exclusions and CUDA/Vulkan comparison. The [pipeline overview](https://huggingface.co/Daecore)
-explains retrieval and selection.
+| 1 | 56.69% → **75.95%** | 56.69% → **75.95%** | 1.44% → **2.12%** | 0.4732 → **0.6527** |
+| 3 | 79.03% → **89.41%** | 56.04% → **75.11%** | 4.23% → **5.89%** | 0.4728 → **0.6424** |
+| 5 | 85.62% → **92.76%** | 55.83% → **73.67%** | 6.74% → **9.32%** | 0.4778 → **0.6373** |
+| 10 | 91.14% → **94.27%** | 54.47% → **70.49%** | 12.83% → **16.99%** | 0.4903 → **0.6377** |
+| 20 | 95.03% → **95.46%** | 52.21% → **64.80%** | 23.63% → **29.63%** | 0.5201 → **0.6489** |
+| 50 | 97.62% → **97.62%** | 42.40% → **42.40%** | 45.83% → **45.83%** | 0.5598 → **0.6125** |
+
+At three results, useful-passage precision changes from **56.04% to
+75.11%**; nDCG@10 changes from **0.4903 to 0.6377**.
+At 50, Hit, precision and recall are identical because every candidate is
+returned. nDCG@50 still measures order: **0.5598 → 0.6125**.
+The pool's **97.62% Hit@50** is a retrieval ceiling, not an Ettin accuracy score.
+
+All three variants use matched **PyTorch FP16** reranking. Depth 1 uses **919 shared judged
+queries** across the three pipeline variants; depths 3–50 use all 925.
+Abstentions are excluded inside the original cutoff without backfill.
+Grades 2–3 count as useful. Precision pools retained positions; recall counts
+known useful passages.
+The expanded reference and matched runtime distinguish this table from the
+earlier CUDA/Vulkan serving check. Daecore returns 3–20 results; these fixed
+cutoffs do not evaluate its selector.
+
+The [evaluation companion](evaluation/README.md) provides
+the paired records, methods and separate serving checks. The
+[pipeline overview](https://huggingface.co/Daecore) explains retrieval and selection.
 
 ## Training data and objective
 

evaluation/README.md

@@ -1,6 +1,6 @@
 # Ettin reranker evaluation
 
-These records support the [model card's](../README.md) ranking results and compare Ettin's CUDA and Vulkan serving paths. They contain anonymized relevance grades and scores, without private queries or passage text.
+These records support the [model card's](../README.md) standalone and full-pipeline ranking results, plus separate CUDA/Vulkan serving checks. They contain anonymized relevance grades and scores, without private queries or passage text.
 
 The [chunking guide](chunking.md#retrieval-model-data) explains the synthetic
 source documents and prepared chunks that form the private candidate pools.
@@ -8,8 +8,9 @@ source documents and prepared chunks that form the private candidate pools.
 | File | Contents |
 |---|---|
 | `reranker.json` | Grades and upstream and fine-tuned scores for 970 fixed pools of 50 passages |
+| `pipeline.json` | Matched component changes, including upstream and fine-tuned Ettin on Gemma v2 + BM25 candidates |
 | `serving.json` | Reviewed top-50 hybrid rankings from CUDA and Vulkan on the same 970 queries |
-| `reranker-summary.json`, `serving-summary.json` | The summaries that `metrics.py` reproduces |
+| `*-summary.json` | The summaries that `metrics.py` reproduces |
 | `serving-qualification.json` | Serving checks, public reranking results and tested limits |
 | `metrics.py`, `figures.py`, `svg_figures.py` | Metric and figure code |
 
@@ -22,7 +23,7 @@ python metrics.py --directory .
 python figures.py --output ../figures
 ```
 
-No packages or network access are needed. The outputs match `reranker-summary.json` and `serving-summary.json`; the second command redraws the card's comparison figure. This recomputes saved results, rather than running inference again. The shared pipeline table uses `serving-summary.json`'s `answerable` section; its outer `models` section preserves all-query coverage.
+No packages or network access are needed. Each output matches its corresponding `*-summary.json`; the second command redraws the card's comparison figure. This recomputes saved results, rather than running inference again. The pipeline card table compares `upstream_ettin` with `finetuned` in `pipeline-summary.json`. The separate serving record preserves both answerable-only and all-query provider results.
 
 ## Ranking quality
 
@@ -48,24 +49,45 @@ queries from an earlier lexical supplement. Its separate held-out artifacts
 are no longer available, so those historical checks cannot be recomputed and
 are not part of the comparison reported here.
 
-## CUDA and Vulkan
-
-Both model cards share the CUDA fixed-depth table from `serving.json`,
-using **925 queries with known grade-2/3 evidence** in a shared reference
-across dense and hybrid retrieval. These queries have **122,231 graded
-query–passage pairs**, within a full reference of 127,665 pairs across 970
-queries. Membership comes from the reference, so pipeline retrieval misses
-remain included. This is a different population and candidate set from
-Ettin's historical 873-answerable-pool table.
+## Effect in the retrieval pipeline
+
+`pipeline.json` compares upstream and fine-tuned Ettin on identical Gemma v2 +
+BM25 candidate pools (`upstream_ettin` → `finetuned`). A third arm changes
+Gemma alone for its companion card; both comparisons end at the same pipeline.
+At 50, Hit, precision and recall must match when only Ettin changes; nDCG
+still measures how well it orders that pool.
+
+The comparison keeps the **925-query cohort frozen before scoring** and uses
+**122,783 graded query–passage pairs** for those queries. It adds 552 grades
+and 10 abstentions to their September reference of 122,231 pairs; old
+grades are unchanged. Two independent GPT-6.1 Sol judges and a fresh blind
+adjudicator resolved the new pairs, with 84 ordinal disagreements.
+The primary assistant reviewed 84 complete pairs, including every final quotation
+flag and abstention plus a stratified sample. These are model judgments,
+without a human reference panel.
+
+All three variants were scored anew in **PyTorch FP16**, using Transformers
+5.2.0, Sentence Transformers 5.5.1, 128 query tokens and 1,024 passage tokens.
+Gemma uses cached FP32 embeddings at 768 dimensions with matched input limits.
+The actual first stage uses the frozen query forms, dense retrieval, BM25,
+reciprocal-rank fusion with constant 60, exact-text deduplication and 50 slots.
+Before substituting upstream Gemma's vectors, replay reproduced all 970 saved
+v2 candidate lists exactly. Ties preserve candidate order.
+
+Depth 1 uses **919 common judged queries across all three arms**; depths
+3–50 retain all 925. Original prefixes are cut before abstentions are removed,
+with no backfill. Retrieval misses remain included. The reference supplies
+known-positive recall and ideal DCG; it is not exhaustive corpus labeling.
+Fixed cutoffs do not test the calibrated selector's choice of 3–20 results.
+The earlier ONNX provider replay retains its own runtime and September
+reference, so its absolute scores should not be substituted into this comparison.
 
-Depths 3–50 use all 925 queries. Depth 1 uses the same 920 for both providers;
-five are omitted because a first result was ungradable. Fixed cutoffs do not
-evaluate the selector's choice of a 3–20-item prefix.
+## CUDA and Vulkan
 
-At 50, the hybrid pool reaches **97.62% Hit and 45.98% known-positive recall**
-on the shared answerable subset. This is candidate coverage against the wider
-reference. Ettin's standalone fixed-pool Hit and recall instead reach 100%
-by construction on its answerable pools.
+The separate provider check retains the September reference: **122,231 graded
+pairs for 925 known-answerable queries**, within 127,665 pairs across all 970.
+Depth 1 uses 920 common judged queries; depths 3–50 use all 925. It does not
+compare upstream with fine-tuned Ettin.
 
 `serving.json` compares CUDA FP16 and Vulkan FP32 on 970 candidate pools retrieved with [Gemma v2](https://huggingface.co/Daecore/embeddinggemma-300m-memory-ft-v2) and BM25. Ettin's learned weights and the score mapping are unchanged. This is a retrieval-pipeline check on fixed inputs; it does not measure a change in Gemma.
 
@@ -89,7 +111,7 @@ and final quotation flag. Sixteen quotation repairs changed no grades. These
 are model judgments without human review.
 
 Existing grades and rankings are unchanged. The earlier reference expansion
-changed recall and ideal DCG; the card now also conditions quality on the
+changed recall and ideal DCG; this provider table conditions quality on the
 shared 925 known-answerable queries. Each provider omits 52 abstained positions
 at rank 20 and 93 at rank 50, with no backfill. Precision pools retained
 positions and nDCG reindexes them. The standalone fixed-pool Ettin comparison
@@ -97,7 +119,7 @@ and public benchmark labels are unchanged.
 
 ### All-query coverage
 
-The saved records also preserve all 970 source queries, including 45 with no
+The September serving record also preserves all 970 source queries, including 45 with no
 known useful evidence. This separate audit view uses the full 127,665-pair
 reference; it does not replace the answerable-only card table.
 

evaluation/metrics.py

@@ -267,10 +267,22 @@ def summarize_serving(data: dict) -> dict:
             'answerable': _summarize_ranked_grades(data, include_selected=True, answerable_only=True)}
 
 
+def summarize_pipeline(data: dict) -> dict:
+    """Matched component changes on a frozen, known-answerable query cohort."""
+    if type(data['corpus_passages']) is not int or data['corpus_passages'] <= 0:
+        raise ValueError('Pipeline comparison requires a positive corpus size')
+    output = _summarize_ranked_grades(data, include_selected=False)
+    if any(row['reference_grade_counts']['2'] + row['reference_grade_counts']['3'] == 0
+           for row in data['rows']):
+        raise ValueError('Pipeline cohort requires known useful evidence for every query')
+    return {**output, 'corpus_passages': data['corpus_passages'],
+            'reference_pairs': sum(sum(row['reference_grade_counts'].values()) for row in data['rows'])}
+
+
 SUMMARIZERS = {
     'classifier': summarize_classifier, 'reranker': summarize_reranker,
     'retriever': summarize_retriever, 'dimensions': summarize_dimensions,
-    'promotion': summarize_promotion, 'serving': summarize_serving,
+    'promotion': summarize_promotion, 'serving': summarize_serving, 'pipeline': summarize_pipeline,
 }
 
 
@@ -283,6 +295,7 @@ def summarize_record(name: str, data: dict) -> dict:
         'dimensions': {'id', 'dataset', 'ndcg@10'},
         'promotion': {'id', 'group', 'reference_grade_counts', 'ranked_grades', 'excluded', 'selected_depth'},
         'serving': {'id', 'group', 'reference_grade_counts', 'ranked_grades', 'excluded', 'selected_depth'},
+        'pipeline': {'id', 'group', 'reference_grade_counts', 'ranked_grades', 'excluded'},
     }[name]
     rows = data['rows']
     if not rows or any(set(row) != fields for row in rows):
@@ -336,7 +349,7 @@ def main() -> None:
     parser = argparse.ArgumentParser(description=__doc__)
     parser.add_argument("--directory", type=Path, default=Path(__file__).resolve().parent)
     parser.add_argument("--output", type=Path)
-    parser.add_argument("--model", choices=("classifier", "reranker", "retriever", "dimensions", "promotion", "serving"), help="Recompute one comparison; default: every record in this package")
+    parser.add_argument("--model", choices=tuple(SUMMARIZERS), help="Recompute one comparison; default: every record in this package")
     args = parser.parse_args()
     names = [args.model] if args.model else [
         name for name in SUMMARIZERS if (args.directory / f"{name}.json").is_file()

evaluation/pipeline-summary.json

@@ -0,0 +1,283 @@
+{
+  "queries": 925,
+  "models": {
+    "upstream_gemma": {
+      "1": {
+        "scored_queries": 919,
+        "excluded_queries": 6,
+        "hit": 0.7399347116430903,
+        "precision": 0.7399347116430903,
+        "macro_precision": 0.7399347116430903,
+        "ndcg": 0.6295144826156795,
+        "known_positive_recall": 0.02009148065967933,
+        "recall_queries": 919,
+        "useful": 680,
+        "retained": 919,
+        "excluded_positions": 0,
+        "mean_useful": 0.7399347116430903,
+        "mean_retained": 1.0
+      },
+      "3": {
+        "scored_queries": 925,
+        "excluded_queries": 0,
+        "hit": 0.8681081081081081,
+        "precision": 0.7149566473988439,
+        "macro_precision": 0.7153153153153153,
+        "ndcg": 0.6120552491562434,
+        "known_positive_recall": 0.05489711276806684,
+        "recall_queries": 925,
+        "useful": 1979,
+        "retained": 2768,
+        "excluded_positions": 7,
+        "mean_useful": 2.1394594594594594,
+        "mean_retained": 2.9924324324324325
+      },
+      "5": {
+        "scored_queries": 925,
+        "excluded_queries": 0,
+        "hit": 0.8994594594594595,
+        "precision": 0.7009527934170636,
+        "macro_precision": 0.7011351351351351,
+        "ndcg": 0.60538850988017,
+        "known_positive_recall": 0.08526196518888282,
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+        "useful": 3237,
+        "retained": 4618,
+        "excluded_positions": 7,
+        "mean_useful": 3.4994594594594592,
+        "mean_retained": 4.992432432432432
+      },
+      "10": {
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+        "excluded_queries": 0,
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+        "precision": 0.6519761775852734,
+        "macro_precision": 0.6521381381381381,
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+        "useful": 6021,
+        "retained": 9235,
+        "excluded_positions": 15,
+        "mean_useful": 6.509189189189189,
+        "mean_retained": 9.983783783783784
+      },
+      "20": {
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+        "excluded_queries": 0,
+        "hit": 0.9567567567567568,
+        "precision": 0.5685743678596568,
+        "macro_precision": 0.5687161650814901,
+        "ndcg": 0.5873176954516321,
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+        "useful": 10501,
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+        "excluded_positions": 31,
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+        "mean_retained": 19.966486486486488
+      },
+      "50": {
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+        "excluded_queries": 0,
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+        "precision": 0.35454801464376234,
+        "macro_precision": 0.35468757089595926,
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+        "mean_retained": 49.905945945945945
+      }
+    },
+    "upstream_ettin": {
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+        "precision": 0.5669205658324266,
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+        "ndcg": 0.4731851391263796,
+        "known_positive_recall": 0.01443636631579192,
+        "recall_queries": 919,
+        "useful": 521,
+        "retained": 919,
+        "excluded_positions": 0,
+        "mean_useful": 0.5669205658324266,
+        "mean_retained": 1.0
+      },
+      "3": {
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+        "excluded_queries": 0,
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+        "precision": 0.5603603603603604,
+        "macro_precision": 0.5603603603603603,
+        "ndcg": 0.4727968405371722,
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+        "mean_useful": 1.681081081081081,
+        "mean_retained": 3.0
+      },
+      "5": {
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+        "mean_retained": 4.997837837837838
+      },
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+      }
+    },
+    "finetuned": {
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+        "ndcg": 0.612510329000538,
+        "known_positive_recall": 0.4583210768438434,
+        "recall_queries": 925,
+        "useful": 19572,
+        "retained": 46157,
+        "excluded_positions": 93,
+        "mean_useful": 21.158918918918918,
+        "mean_retained": 49.89945945945946
+      }
+    }
+  },
+  "corpus_passages": 82719,
+  "reference_pairs": 122783
+}

publication-manifest.json

@@ -1,6 +1,6 @@
 {
   "schema": "daecore.retrieval-publication-manifest",
-  "tool_sha256": "46dcb4b17750d5a4ba3d44b1236f33e9e5657673b648ae6cad48fe4ac4f147f8",
+  "tool_sha256": "60165b669f2cce17d8830fe9b712fb60a8e82280c54b224d71a1ce8fd82142ac",
   "model_id": "ettin-150m-memory-reranker-ft-v1",
   "public_repo": "Daecore/ettin-150m-memory-reranker-ft-v1",
   "role": "cross-encoder-reranker",
@@ -26,7 +26,7 @@
       }
     }
   },
-  "staged_at": "2026-09-30T02:12:28+00:00",
+  "staged_at": "2026-10-01T16:23:47+00:00",
   "files": {
     "config.json": {
       "sha256": "a897acffc99f97238bb5bc0e1bccd6b7fe8fdfebe1450d4db3a9ab026cce6795",
@@ -89,8 +89,8 @@
       "binding": "packaging record"
     },
     "evaluation/README.md": {
-      "sha256": "de60f0b772883cce4b15269b3476a2d30953ea63fa23252675fb755a3d939515",
-      "size": 7903,
+      "sha256": "5399753e1e6e28be3997c1ebf4ee017bfda96d61950bebd89f2b175b29512cad",
+      "size": 9377,
       "binding": "packaging record"
     },
     "evaluation/serving-qualification.json": {
@@ -99,8 +99,8 @@
       "binding": "packaging record"
     },
     "evaluation/metrics.py": {
-      "sha256": "22536c4437be0b8c678df0dee4c45e31c5413e91f1fecf104750486f3e372d46",
-      "size": 19120,
+      "sha256": "8123f75da6edb158db74c62c1c65b6eefbc2713572409ac8a9bfb5424497aaf1",
+      "size": 19927,
       "binding": "packaging record"
     },
     "evaluation/chunking.md": {
@@ -118,6 +118,11 @@
       "size": 14521,
       "binding": "packaging record"
     },
+    "evaluation/pipeline-summary.json": {
+      "sha256": "5f28ae25b5db2f4c0a8d11891df7117f95f644063092d58ba45e27a59747e3a2",
+      "size": 9373,
+      "binding": "packaging record"
+    },
     "evaluation/reranker.json": {
       "sha256": "20b5760a462c3c2d2281607f2c5045d3f60210123a0c11c8b2d92580c3cccd3e",
       "size": 1145459,
@@ -128,6 +133,11 @@
       "size": 1184937,
       "binding": "packaging record"
     },
+    "evaluation/pipeline.json": {
+      "sha256": "8f140901a6b151d9db97fd109a3774bf1c046b849d9a01c874cb0efdea297300",
+      "size": 1614738,
+      "binding": "packaging record"
+    },
     "evaluation/figures.py": {
       "sha256": "f13945b350e007deb18a2eafbe1c5708a71b9df62ee12b6833d07f3e2a06094e",
       "size": 5490,
@@ -144,10 +154,10 @@
       "binding": "packaging record"
     },
     "README.md": {
-      "sha256": "7f08c4025cd386a64b904f0bb038af20df6c2bcf56aef1401dae4293a3fb22cb",
-      "size": 13036,
+      "sha256": "c8a24bd6c07a34e68d33ba94e476dd75634a72660b21e575435865c25f0002f4",
+      "size": 13443,
       "binding": "model card with upload-relative links",
-      "source_sha256": "accc91b7d2afd4d6097dcc8f6038ddddfdf1ca7faf9fb09e09bdf0ec5d90e9cb"
+      "source_sha256": "d3c45681e0ba1ef22b0bfe9eef372b298e1223082e790e25ace134d6a6c760e3"
     }
   }
 }