Add the Jev false-negative judgments (judgments config)

{
"judgments": {
"rows_in_run": 2913969,
"rows_dropped_missing": 0,
"queries_dropped": 0,
"rows": 2913969,
"queries": 100000,
"positives": 100000,
"candidates_per_query": 28.1,
"top_up_rows": 869144
}
}

README.md

@@ -27,6 +27,10 @@ configs:
     path: hard-negatives/test-*.parquet
   - split: train
     path: hard-negatives/train-*.parquet
+- config_name: judgments
+  data_files:
+  - split: train
+    path: judgments/train-*.parquet
 - config_name: qrels
   data_files:
   - split: dev
@@ -65,6 +69,7 @@ in this collection.
 | Splits | `corpus`: train · `hard-negatives`: train, dev, test · `qrels`: train, dev, test · `queries`: train, dev, test · `teacher-scores`: train |
 | Hard negatives | sources: `dataset`, `dense` · 10,243,224 rows |
 | Teacher scores | `jinaai/jina-reranker-v3.5` · 10,091,625 rows (positives included) |
+| Judgments | `judgments`: `typesafe/jev-1.13.0` · 2,913,969 rows |
 | Ids | `sha1(text)[:20]`; identical texts collapse to one document (849,330 collapsed) |
 | Pair recovery | 671,325 of 671,325 source pairs reconstructed from `queries` × `qrels` × `corpus` with byte-equal text |
 | Direction | symmetric source: the first text is the query, the second the document — a convention, both texts are in the corpus |
@@ -79,6 +84,7 @@ in this collection.
 | `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 mined 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: float32`, `round: int32` | one row per judged pair; `role` is `positive` (the training positive) or `candidate` (a mined candidate, never a labelled negative); `p_yes` in [0, 1]; `round` 0 the first request, 1.. top-ups |
 
 Files are Parquet, sorted by id, zstd-compressed, sharded at 500 MB. Every rule above is enforced by a validator
 before publishing; `provenance.json` records the source file hashes, what changed, and the output file hashes.
@@ -110,6 +116,18 @@ negatives = load_dataset("Hyukkyu/train-all-nli", "hard-negatives", split="train
 scores    = load_dataset("Hyukkyu/train-all-nli", "teacher-scores", split="train")
 ```
 
+## Jev judgments
+
+The `judgments` config holds, for every training query, 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: its training positive, its candidates whose teacher score is at least 0.85 × the positive's (the 24 highest) and 8 random candidates below that, shuffled under neutral ids, one yes/no question per passage (`round` 0). Queries left with fewer than 7, then 10, candidates under the cutoff got the next hardest unjudged candidates in top-up rounds (`round` 1–8). The dataset's own labelled negatives were never sent. Texts were cut to 256 (query) and 512 (passage) tokens of the `jina-embeddings-v5` small tokenizer. Grouped judgments run about 0.07 below single-pair ones, so the thresholds below apply to this table.
+- **Accuracy** (blind-labelled audit of 1,946 pairs): a candidate at or above its source's cutoff is relevant 73% of the time, one below it 9%; a positive under 0.15 is mislabelled 80% of the time, except in AG News, NPR and HotpotQA, whose positives are right by construction (Jev's flags there were 0–17% right).
+- **Use** (the SPARSE loader, `annotation.filter.judge`): a candidate at P(yes) ≥ the cutoff 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.
+
+| config | queries | rows | candidates per query | top-up rows | candidate cutoff | positive check |
+|---|---:|---:|---:|---:|---:|---|
+| `judgments` | 100,000 | 2,913,969 | 28.1 | 869,144 | 0.47 | < 0.15 |
+
 ## Load it
 
 ```python

judgments/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bf1dda90e3e6f404f7554a45b2ff615990ad866718109b2cbfcefbf874941162
+size 36147382