text2sql: re-mined candidates (100 to depth 1000); teacher scores to come

README.md

@@ -49,8 +49,8 @@ A seeded sample of [`gretelai/synthetic_text_to_sql`](https://huggingface.co/dat
 | Score values | 2 ×30,000 (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 |
-| Hard negatives | sources: `dense` · 1,769,501 rows |
-| Teacher scores | `jinaai/jina-embeddings-v5-text-small` (the miner's cosine, not a reranker) · 1,799,501 rows (positives included) |
+| Hard negatives | sources: `dense` · 2,970,047 rows |
+| Teacher scores | none yet (0 rows): `jinaai/jina-reranker-v3.5` scores come next |
 | Ids | `sha1(text)[:20]`; identical texts collapse to one document / query |
 | License | `apache-2.0` |
 
@@ -78,15 +78,14 @@ Files are Parquet, sorted by id, zstd-compressed, sharded at 500 MB. Every rule
 
 ## Hard negatives and teacher scores
 
-Filled by the owner's domain pipeline (`convert_domain.py`, `mine_domain.py`), not yet by the annotation pipeline of the other repositories in this collection:
+Filled by the collection's annotation pipeline (`annotation=jina35`). **Interim**: the candidates are final, the teacher scores are still to come.
 
-- **Corpus**: the source's own passages (its positives and the negatives it provides), not a larger collection.
-- **Candidates**: dense retrieval with `jinaai/jina-embeddings-v5-text-small` (full length) over that corpus to depth 300; 60 candidates per query drawn from the rank windows 1–30 (30), 31–100 (20), 101–300 (10), the query's positives excluded. `rank` is the dense rank; `source` is `dense` for a mined row and `dataset` for a negative the source labels itself (ranked after the mined ones).
-- **Teacher scores**: the miner's cosine between the query and passage embeddings, one row per (query, positive) and per (query, candidate), `teacher` = `jinaai/jina-embeddings-v5-text-small`. **Not a reranker**: the other repositories carry `jinaai/jina-reranker-v3.5` scores. Re-mining with the collection's plan (100 candidates to depth 1,000) and re-scoring with that reranker are under way; they will replace these two tables, and this section will say so.
+- **Candidates**: dense retrieval with `jinaai/jina-embeddings-v5-text-small` over this corpus to depth 1,000; 100 candidates per query drawn from the rank windows 1–30 (30), 31–100 (30), 101–300 (20), 301–1000 (20), the query's labelled positives excluded. `rank` is the dense rank; `source` is `dense` for a mined row and `dataset` for a negative the source labels itself.
+- **Teacher scores**: none yet. `teacher-scores` holds 0 rows until the `jinaai/jina-reranker-v3.5` scores (listwise, as in the other repositories) are filled in; `datasets` cannot return a 0-example split, so read that file with `pyarrow` / `pandas` meanwhile. The candidates stay.
 
 | configs | queries | hard negatives | teacher scores |
 |---|---:|---:|---:|
-| `hard-negatives` · `teacher-scores` | 30,000 (all) | 1,769,501 (1,769,501 dense) | 1,799,501 |
+| `hard-negatives` · `teacher-scores` | 30,000 (all) | 2,970,047 (2,970,047 dense) | 0 |
 
 ## Load it
 

hard-negatives/train-00000-of-00001.parquet

@@ -1,3 +1,3 @@
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-oid sha256:73687cdf78d6269dde3db6bead258239d22539715190f670bc973c0838679d33
-size 6259488
+oid sha256:bd5f03c4b33bcd7c286f360068e4f2b56a3365594864519207c175ce43cc00f1
+size 10608876

provenance.json

@@ -131,5 +131,26 @@
     }
    ]
   }
+ },
+ "annotation": {
+  "namespace": "jina35-u2",
+  "stage": "mined; teacher scores to come",
+  "miner": "jinaai/jina-embeddings-v5-text-small",
+  "dense_top": 1000,
+  "sample": {
+   "dense_1_30": 30,
+   "dense_31_100": 30,
+   "dense_101_300": 20,
+   "dense_301_1000": 20
+  },
+  "stats": {
+   "queries": 30000,
+   "candidates": 2970047,
+   "by_source": {
+    "dense": 2970047
+   },
+   "scores": 0,
+   "positives": 30000
+  }
  }
 }

teacher-scores/train-00000-of-00001.parquet

@@ -1,3 +1,3 @@
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-oid sha256:9a3e81b3c6d9c9b143e4de4c3d083fc8dbff48a8dab81e2b52bbe6792a2785d7
-size 11828501
+oid sha256:8316266e7cbe6867227eefc104eb93ee2252b885a6127a24b8e5c1982716406a
+size 815