Jev false-negative judgments (judgments config)

{
"judgments": {
"rows_in_run": 844202,
"rows_dropped": 0,
"queries_removed": 0,
"queries_trimmed": 0,
"rows": 844202,
"queries": 59997,
"positives": 59997,
"candidates_per_query": 13.1,
"top_up_rows": 523425,
"judge": "typesafe/jev-1.13.0"
}
}

README.md

@@ -22,6 +22,10 @@ configs:
   data_files:
   - split: train
     path: hard-negatives/train-*.parquet
+- config_name: judgments
+  data_files:
+  - split: train
+    path: judgments/train-*.parquet
 - config_name: qrels
   data_files:
   - split: train
@@ -48,9 +52,10 @@ A seeded sample of [`ise-uiuc/Magicoder-OSS-Instruct-75K`](https://huggingface.c
 | Qrels per query | min 1 · mean 1.0 · max 1 |
 | Score values | 2 ×59,997 (2: the first positive, 1: any other) |
 | Layout | `queries` · `corpus` · `qrels` · `hard-negatives` · `teacher-scores`, split `train` |
-| Splits | `corpus`: train · `hard-negatives`: train · `qrels`: train · `queries`: train · `teacher-scores`: train |
+| Splits | `corpus`: train · `hard-negatives`: train · `judgments`: train · `qrels`: train · `queries`: train · `teacher-scores`: train |
 | Hard negatives | sources: `dense` · 5,939,734 rows |
 | Teacher scores | `jinaai/jina-reranker-v3.5` · 5,999,731 rows (positives included) |
+| Judgments | `judgments`: `typesafe/jev-1.13.0` · 844,202 rows |
 | Ids | `sha1(text)[:20]`; identical texts collapse to one document / query |
 | License | `mit` |
 
@@ -63,6 +68,7 @@ A seeded sample of [`ise-uiuc/Magicoder-OSS-Instruct-75K`](https://huggingface.c
 | `qrels` | `query-id: string`, `corpus-id: string`, `score: int32` | referential integrity to both tables; no duplicate pairs; no floats |
 | `hard-negatives` | `query-id: string`, `corpus-id: string`, `rank: int32`, `source: string` | one row per negative; `(query-id, corpus-id, source)` unique; never a labelled positive of the same query |
 | `teacher-scores` | `query-id: string`, `corpus-id: string`, `teacher: string`, `score: float32` | one row per scored pair (positives included); a row *means* scored — never a placeholder |
+| `judgments` | `query-id: string`, `corpus-id: string`, `judge: string`, `role: string`, `p_yes: float64`, `round: int32` | one row per judged pair; `role` is `positive` (the training positive) or `candidate` (a mined candidate, never a labelled positive or a labelled negative); `p_yes` in [0, 1]; `round` 0 the first request, 1.. the top-ups |
 
 Files are Parquet, sorted by id, zstd-compressed, sharded at 500 MB. Every rule above is checked before publishing; `provenance.json` records the source revision, what changed, and the output file hashes.
 
@@ -100,6 +106,18 @@ negatives = load_dataset("Hyukkyu/train-magicoder", "hard-negatives", split="tra
 scores    = load_dataset("Hyukkyu/train-magicoder", "teacher-scores", split="train")
 ```
 
+## Jev judgments
+
+`judgments` holds, for every query of the training sample (the queries with teacher scores), whether [TypeSafe](https://typesafe.ai)'s Jev (`jev-1.13.0`) judged its training positive and its mined candidates relevant: `p_yes` is Jev's P(yes) for the source's question (e.g. *does the passage answer the query?*). They locate the false negatives among the mined candidates and the mislabelled positives.
+
+- **Requests.** One request per query (`round` 0): its training positive, its candidates whose teacher score taken as (cos + 1) / 2 is at least 0.85 × the positive's (at most 12, the highest scores) and 4 random candidates below that, shuffled under neutral ids, one yes/no question per passage. Queries left with fewer than 10 candidates under their source's cutoff got their next hardest unjudged candidates in rounds 1–8 (8 per request), those still under 10 in rounds 9–10 (24 per request). The dataset's own labelled negatives were never sent. Texts were cut to 512 (query) and 512 (passage) tokens of the `jina-embeddings-v5` small tokenizer. Jev answers a request's passages in one context, so P(yes) is calibrated to these groups: the thresholds below apply to this table, not to single-pair calls.
+- **Accuracy** (an audit of 1237 pairs from the pilot's first requests (100 queries per source; the five long-query sources re-piloted at 512-token queries), labelled blind by an LLM (Claude), at the pilot's fixed thresholds 0.35 and 0.15): a candidate at P(yes) ≥ 0.35 was relevant 67% of the time inside the band (n = 350) and 45% below it (n = 87); one under 0.35 was relevant 6% (band, n = 387) and 1% (below the band, n = 210) of the time. A positive under 0.15 was mislabelled 100% of the time (n = 20) in the sources that keep the check; in dom-casehold, dom-clerc, dom-cornstack-py, dom-finqa10k, dom-gerlayqa, dom-investopedia, dom-lawse, dom-magicoder, dom-medmcqa, dom-pubmedqa, dom-s2orc, dom-tatqa, Jev's flags were right less often (0%–57% in this audit), under the 70% the check needs, so their positives are not checked.
+- **Use** (the SPARSE loader, `annotation.filter.judge`): a candidate at P(yes) ≥ its source's cutoff (below; fitted on 1,521 labelled pairs) is never a negative; a positive under 0.15 is replaced by the candidate Jev scores highest if that is ≥ 0.8, else the query is dropped; a candidate at ≥ 0.9 can become an extra positive. Compare `p_yes` as a float64 (it is stored as one).
+
+| config | queries | rows | candidates per query | top-up rows | candidate cutoff | positive check |
+|---|---:|---:|---:|---:|---:|---|
+| `judgments` | 59,997 | 844,202 | 13.1 | 523,425 | 0.32 | skipped |
+
 ## Load it
 
 ```python
@@ -109,6 +127,7 @@ corpus    = load_dataset("Hyukkyu/train-magicoder", "corpus", split="train")
 qrels     = load_dataset("Hyukkyu/train-magicoder", "qrels", split="train")
 negatives = load_dataset("Hyukkyu/train-magicoder", "hard-negatives", split="train")
 scores    = load_dataset("Hyukkyu/train-magicoder", "teacher-scores", split="train")
+judgments = load_dataset("Hyukkyu/train-magicoder", "judgments", split="train")
 ```
 
 ## License and attribution

judgments/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:728bbaa67ac530ca16bdcb570a78bdef15fd613d9f83c249029e804a143a5654
+size 9792100