Chunk training splits at fixed task sizes

README.md

@@ -177,8 +177,8 @@ row. Groups with 3–9 rows receive one validation and one test row; groups with
 fewer than three stay in training. Verse syntax and metre rows are split
 independently, so linked alignment components need not have identical split
 labels. The 100-row eligibility and remainder bottleneck is then applied to all
-three task sizes. Training is left atomic, while the equally sized validation
-and test source sets are exact multiples of 100 per author. This makes the
+three task sizes. The train, validation, and test source sets are all exact
+multiples of 100 per author. This makes the
 published source-row ratio slightly different from the initial 80/10/10 split.
 A stricter work-held-out evaluation can be added as a later track.
 
@@ -192,11 +192,10 @@ All configurations contain:
   revision, URL, license, annotation provenance and syntax scheme;
 - `licenses`, `dedup_key`, and `split`.
 
-`text` is always a JSON list of strings. Atomic `_1` rows and training rows
-contain one string; validation and test rows in `_10` and `_100` contain
-exactly 10 or 100 separate sentence strings (`verse_sentence` and `prose`) or
-line strings (`verse_metre`). No whitespace separator is used to encode unit
-boundaries.
+`text` is always a JSON list of strings. Atomic `_1` rows contain one string;
+every split of `_10` and `_100` contains exactly 10 or 100 separate sentence
+strings (`verse_sentence` and `prose`) or line strings (`verse_metre`). No
+whitespace separator is used to encode unit boundaries.
 
 `conllu` is likewise always a JSON list of strings with the same number of
 units as `text`. Each `verse_metre` CoNLL-U unit is cropped to its corresponding
@@ -205,8 +204,8 @@ metrical line, so it cannot expose words beyond that line.
 The `_10` and `_100` configurations additionally contain `chunk_size`,
 `chunk_target_size`, ordered `constituent_ids`, JSON
 `constituent_provenance`, `chunk_work_ids`, `chunk_works`, and
-`chunk_is_mixed_work`. Training rows have `chunk_size = 1`; validation and test
-rows always have `chunk_size` equal to the configuration suffix. Aggregated
+`chunk_is_mixed_work`. Every row has `chunk_size` equal to the configuration
+suffix. Aggregated
 `text` and `conllu` follow constituent order, while constituent provenance
 retains the original passage-level locations.
 

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inspection/README.md

@@ -10,7 +10,7 @@ text. `_split_row_index` records the original physical row order within each
 split. `_mirror_manifest` records row counts, column lists, and content hashes.
 
 The top-level `text` column is a JSON list: one string for atomic rows and one
-string per constituent sentence or line for evaluation chunks. For example,
+string per constituent sentence or line for fixed-size chunks. For example,
 retrieve the ten separate sentences in the first validation row as follows:
 
 ```sh

inspection/sphragis.sqlite

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metadata/build_report.json

@@ -540,47 +540,47 @@
   "data_files": {
     "prose_1": {
       "test": 2800,
-      "train": 25730,
+      "train": 25200,
       "validation": 2800
     },
     "prose_10": {
       "test": 280,
-      "train": 25730,
+      "train": 2520,
       "validation": 280
     },
     "prose_100": {
       "test": 28,
-      "train": 25730,
+      "train": 252,
       "validation": 28
     },
     "verse_metre_1": {
       "test": 2600,
-      "train": 21818,
+      "train": 21700,
       "validation": 2600
     },
     "verse_metre_10": {
       "test": 260,
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+      "train": 2170,
       "validation": 260
     },
     "verse_metre_100": {
       "test": 26,
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+      "train": 217,
       "validation": 26
     },
     "verse_sentence_1": {
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-      "train": 11914,
+      "train": 11800,
       "validation": 1400
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     "verse_sentence_10": {
       "test": 140,
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+      "train": 1180,
       "validation": 140
     },
     "verse_sentence_100": {
       "test": 14,
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+      "train": 118,
       "validation": 14
     }
   },
@@ -922,24 +922,25 @@
     ]
   },
   "splitting": {
-    "evaluation_chunking": {
+    "chunking": {
       "ordering": "natural passage or line order within canonical work ID",
       "remainder_policy": "discard n modulo 100 rows per author and split by stable hash once; reuse the retained atomic rows in every task size",
       "seed": 776,
-      "training_policy": "retain 100-task-eligible-author training rows as atomic units in all tasks",
+      "training_policy": "chunk training identically to validation and test; augmentation is model-side",
       "work_policy": "emit complete single-work chunks first, then combine residual work tails"
     },
     "seed": 776,
     "strategy": "independent row-level 80/10/10 stratification by author and work",
     "variants": {
       "_1": "shared 100-task-eligible atomic rows",
-      "_10": "shared atomic training rows; fixed exact 10-row evaluation chunks",
-      "_100": "shared atomic training rows; fixed exact 100-row evaluation chunks"
+      "_10": "fixed exact 10-row chunks in every split",
+      "_100": "fixed exact 100-row chunks in every split"
     }
   },
   "validation": {
     "checks": {
       "complete_chunk_text_and_syllable_aggregation": true,
+      "fixed_exact_chunks_all_splits": true,
       "fixed_exact_evaluation_chunks": true,
       "identical_source_rows_across_task_sizes": true,
       "identifier_free_conllu_comments": true,
@@ -953,136 +954,136 @@
       "scansion_column_absent": true,
       "sentence_local_metrical_annotations": true,
       "source_verse_component_exact_coverage": true,
-      "unique_conllu_documents_checked": 71899,
+      "unique_conllu_documents_checked": 71146,
       "unique_ids": true
     },
     "prose_1": {
       "authors": 12,
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+      "rows": 30800,
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-        "ud_perseus": 4306,
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         "validation": 2800
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       "works": 39
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@@ -856,7 +1388,7 @@
     "row_unit": "sentence",
     "rows": {
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-      "train": 25730,
+      "train": 25200,
       "validation": 2800
     },
     "shared_source_selection_target": 100
@@ -1283,6 +1815,538 @@
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@@ -1704,17 +2768,18 @@
     },
     "discarded_source_rows": {
       "test": 416,
+      "train": 530,
       "validation": 416
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     "minimum_validation_and_test_source_rows_per_author": 100,
     "mixed_work_chunks": {
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     "retained_authors": [
@@ -1731,13 +2796,13 @@
       "Thucydides",
       "Xenophon"
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@@ -2161,6 +3226,538 @@
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@@ -2582,17 +4179,18 @@
     },
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     "mixed_work_chunks": {
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@@ -2609,13 +4207,13 @@
       "Thucydides",
       "Xenophon"
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@@ -2749,6 +4347,126 @@
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@@ -2886,7 +4604,7 @@
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@@ -3024,6 +4742,126 @@
         "vm-ff409e2eedfa7aa7008f",
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@@ -3156,27 +4994,27 @@
     },
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       "validation": 127
     },
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     "mixed_work_chunks": {},
     "represented_source_rows": {
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     "retained_authors": [
       "Homeric-Iliad",
       "Homeric-Odyssey"
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-    "train_row_unit": "line"
+    "shared_source_selection_target": 100
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@@ -3311,6 +5149,126 @@
         "vm-ff409e2eedfa7aa7008f",
         "vm-fff16dc13fb53be666e5"
       ],
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+        "vm-da2b8b386bbfd9558c0a",
+        "vm-dc1f749c72f5e70dab11",
+        "vm-dcc61f142620dda024ed",
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+        "vm-debc3d825d68914bd76a",
+        "vm-dfb1e5e78f0479d945c3",
+        "vm-e127f2461e7a99d919b6",
+        "vm-e264844b90df9e1af894",
+        "vm-e36e68e9d7b41e4f8b6a",
+        "vm-e4119f5d62b7c63de4bd",
+        "vm-e86594817c95088c0002",
+        "vm-e8ab43903ae0fc5d5b3d",
+        "vm-eaeedfabb383b339e5b9",
+        "vm-eb953f16275ad85937fc",
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@@ -3443,27 +5401,27 @@
     },
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     "mixed_work_chunks": {},
     "represented_source_rows": {
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+      "train": 21700,
       "validation": 2600
     },
     "retained_authors": [
       "Homeric-Iliad",
       "Homeric-Odyssey"
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-    "shared_source_selection_target": 100,
-    "train_row_unit": "line"
+    "shared_source_selection_target": 100
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@@ -3559,6 +5517,122 @@
         "vs-fddcc8d291315793d649",
         "vs-fe9e5881c6195442c735"
       ],
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+        "vs-f23dc54f2b5c9b1fd1dc",
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+        "vs-f44a63b6850fb59825ea",
+        "vs-f4aca4963551e029e128",
+        "vs-f9a131a02a95831aeec5",
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@@ -3658,7 +5732,7 @@
     "row_unit": "sentence",
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-      "train": 11914,
+      "train": 11800,
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@@ -3758,6 +5832,122 @@
         "vs-fddcc8d291315793d649",
         "vs-fe9e5881c6195442c735"
       ],
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+        "vs-1197673c9562f5c6b9fc",
+        "vs-1207de349f47db8de4cf",
+        "vs-12ea90dc3c4e54e34ee1",
+        "vs-14ef833adc0777fc0600",
+        "vs-1739bd3de5bf480d7711",
+        "vs-17a60f428433781fcc69",
+        "vs-1864cd1b319a1883742d",
+        "vs-1af670afb5781389f368",
+        "vs-1dd323f90bacf8652eb7",
+        "vs-1ec2db7f2a904e23d5ed",
+        "vs-266e0c31c0223539355d",
+        "vs-28f461a7587870318eb9",
+        "vs-2a3c3b9325e66cc086c5",
+        "vs-2b75f50d037e44e286a9",
+        "vs-2e8161d5f7983af002ca",
+        "vs-3116b57384537ea31cb2",
+        "vs-31a6c0e2cb0a372ff413",
+        "vs-31d69d5a67a22cd7d035",
+        "vs-3787f8bb3e95af2dea79",
+        "vs-389515fc37c12dc2ac30",
+        "vs-3abaad12c78d7826b425",
+        "vs-3b4f6403f8c8d8e938a5",
+        "vs-3d3573e6bfee82207f25",
+        "vs-3d9c8a84af3e8f8cacfc",
+        "vs-3e7d40f4bcc5c03cb404",
+        "vs-3fa51e2225949e7f718f",
+        "vs-412b06550aeb37243c5b",
+        "vs-419af59bac94186446dd",
+        "vs-42ea69bc6dc207eb43d1",
+        "vs-458e74d4711e8f3c6e30",
+        "vs-480ea242154791d65673",
+        "vs-4981486933a486d4fc53",
+        "vs-50eda8744a184c1458f0",
+        "vs-574d3a33e982d071f753",
+        "vs-593daf6ab0d17ce7d483",
+        "vs-5b19fced341a92b587c8",
+        "vs-5cdcfb24902e2d8e5a04",
+        "vs-601c5a1c456b4188f523",
+        "vs-60de7618a9062436d671",
+        "vs-66c9c467f9c2f24379ab",
+        "vs-6778979119ec7f1a8218",
+        "vs-6807899790ede5518b5a",
+        "vs-6aab7cbcd6c37dfee8b2",
+        "vs-6ac38b8ea12cb2dae836",
+        "vs-6b7ba780749b6eeb038f",
+        "vs-6b82b7c08dcc4a3507e0",
+        "vs-6b8b13166ad28e431d9b",
+        "vs-6ba31635b5438d688ad1",
+        "vs-6bec44d6a7cd1e9a4e9d",
+        "vs-6ddc2c490370157ddffc",
+        "vs-6e6c5ec80138b338ae7e",
+        "vs-70f786a599a2945232f7",
+        "vs-7793fffa555ffc2d1de4",
+        "vs-77d2cf3f4c3b8fb5a717",
+        "vs-80404dd4cd4945b6a84f",
+        "vs-81d28d39b0e9c0461dbb",
+        "vs-85a11937b119d2e172ff",
+        "vs-87e3a157c4bf4020c3e2",
+        "vs-888daac8864ea97b888b",
+        "vs-8c065834d1dd7b31ed06",
+        "vs-91611a5ea572fe44eb85",
+        "vs-968da552067676e21156",
+        "vs-9b37c8c280e1f8fb2931",
+        "vs-9bdf22f48d87e53232d3",
+        "vs-a08267e708a0d062deda",
+        "vs-a1b8c129c1c802a9c4df",
+        "vs-a2ac40a292ff2188faad",
+        "vs-a43661449cc1260af65e",
+        "vs-aa677af08dcffd8c7850",
+        "vs-b527dbdd1b2a42673279",
+        "vs-b7b4e3c69344f5e15aeb",
+        "vs-bb8c7c5a7d11eb12af62",
+        "vs-bddb8426bd3209b2ce3d",
+        "vs-be64d48a1087d11a583e",
+        "vs-be910d106122a205eb3c",
+        "vs-c3084b79e88b358909ef",
+        "vs-c335a6a4646d6cd4361d",
+        "vs-c41279c9ae0ed7625836",
+        "vs-c53e2d3a93e877758a73",
+        "vs-c58ab96ccce88ffc79a0",
+        "vs-c7f922bdfcc208735ab6",
+        "vs-c9c578b99f61b51e35c1",
+        "vs-c9f8c3785afd8dc1362f",
+        "vs-cfaf9657408df6c783cb",
+        "vs-d10aa2de8f64e530fedc",
+        "vs-d17283e1f0838c113fc3",
+        "vs-d1e040e5472107601330",
+        "vs-d4e59c99308f14e2d4e9",
+        "vs-d7920cd092fe7976a479",
+        "vs-d858a4c6917abc83544b",
+        "vs-de203e6a56331f4af8ab",
+        "vs-e11c24da1bfa8b1ae9ed",
+        "vs-e9dc7a1354bf81e76fc9",
+        "vs-ee58e7f1a9f9b0a75943",
+        "vs-ee7397fc6a7042647326",
+        "vs-f23dc54f2b5c9b1fd1dc",
+        "vs-f24accf9e5e6aeef9dc7",
+        "vs-f44a63b6850fb59825ea",
+        "vs-f4aca4963551e029e128",
+        "vs-f9a131a02a95831aeec5",
+        "vs-fac24ed2aec335a1e7aa",
+        "vs-facff9964e42ac52a42d",
+        "vs-fb4aa1bcac10abea1c04",
+        "vs-fd8a3bf76b544db6f709",
+        "vs-fd965193aee5960e898d",
+        "vs-fe8058124eefda2a2de5",
+        "vs-ff960d306f71c56d677b"
+      ],
       "validation": [
         "vs-008aff2c83f8804bbe90",
         "vs-01984005c93ea05a5b8f",
@@ -3852,27 +6042,27 @@
     },
     "discarded_source_rows": {
       "test": 89,
+      "train": 114,
       "validation": 89
     },
-    "evaluation_row_unit": "10-sentence chunk",
     "minimum_validation_and_test_source_rows_per_author": 100,
     "mixed_work_chunks": {},
     "represented_source_rows": {
       "test": 1400,
-      "train": 11914,
+      "train": 11800,
       "validation": 1400
     },
     "retained_authors": [
       "Homeric-Iliad",
       "Homeric-Odyssey"
     ],
+    "row_unit": "10-sentence chunk",
     "rows": {
       "test": 140,
-      "train": 11914,
+      "train": 1180,
       "validation": 140
     },
-    "shared_source_selection_target": 100,
-    "train_row_unit": "sentence"
+    "shared_source_selection_target": 100
   },
   "verse_sentence_100": {
     "authors": 2,
@@ -3969,6 +6159,122 @@
         "vs-fddcc8d291315793d649",
         "vs-fe9e5881c6195442c735"
       ],
+      "train": [
+        "vs-01a1f90bcb19951e10ab",
+        "vs-0428fdb4382136e8e429",
+        "vs-049325fd4a174808d308",
+        "vs-0610e9975ac0fda0c3a3",
+        "vs-07c5a38be47ca22e53a5",
+        "vs-0850f2b3f4598c4b93f4",
+        "vs-09c111acb2db807331fd",
+        "vs-1197673c9562f5c6b9fc",
+        "vs-1207de349f47db8de4cf",
+        "vs-12ea90dc3c4e54e34ee1",
+        "vs-14ef833adc0777fc0600",
+        "vs-1739bd3de5bf480d7711",
+        "vs-17a60f428433781fcc69",
+        "vs-1864cd1b319a1883742d",
+        "vs-1af670afb5781389f368",
+        "vs-1dd323f90bacf8652eb7",
+        "vs-1ec2db7f2a904e23d5ed",
+        "vs-266e0c31c0223539355d",
+        "vs-28f461a7587870318eb9",
+        "vs-2a3c3b9325e66cc086c5",
+        "vs-2b75f50d037e44e286a9",
+        "vs-2e8161d5f7983af002ca",
+        "vs-3116b57384537ea31cb2",
+        "vs-31a6c0e2cb0a372ff413",
+        "vs-31d69d5a67a22cd7d035",
+        "vs-3787f8bb3e95af2dea79",
+        "vs-389515fc37c12dc2ac30",
+        "vs-3abaad12c78d7826b425",
+        "vs-3b4f6403f8c8d8e938a5",
+        "vs-3d3573e6bfee82207f25",
+        "vs-3d9c8a84af3e8f8cacfc",
+        "vs-3e7d40f4bcc5c03cb404",
+        "vs-3fa51e2225949e7f718f",
+        "vs-412b06550aeb37243c5b",
+        "vs-419af59bac94186446dd",
+        "vs-42ea69bc6dc207eb43d1",
+        "vs-458e74d4711e8f3c6e30",
+        "vs-480ea242154791d65673",
+        "vs-4981486933a486d4fc53",
+        "vs-50eda8744a184c1458f0",
+        "vs-574d3a33e982d071f753",
+        "vs-593daf6ab0d17ce7d483",
+        "vs-5b19fced341a92b587c8",
+        "vs-5cdcfb24902e2d8e5a04",
+        "vs-601c5a1c456b4188f523",
+        "vs-60de7618a9062436d671",
+        "vs-66c9c467f9c2f24379ab",
+        "vs-6778979119ec7f1a8218",
+        "vs-6807899790ede5518b5a",
+        "vs-6aab7cbcd6c37dfee8b2",
+        "vs-6ac38b8ea12cb2dae836",
+        "vs-6b7ba780749b6eeb038f",
+        "vs-6b82b7c08dcc4a3507e0",
+        "vs-6b8b13166ad28e431d9b",
+        "vs-6ba31635b5438d688ad1",
+        "vs-6bec44d6a7cd1e9a4e9d",
+        "vs-6ddc2c490370157ddffc",
+        "vs-6e6c5ec80138b338ae7e",
+        "vs-70f786a599a2945232f7",
+        "vs-7793fffa555ffc2d1de4",
+        "vs-77d2cf3f4c3b8fb5a717",
+        "vs-80404dd4cd4945b6a84f",
+        "vs-81d28d39b0e9c0461dbb",
+        "vs-85a11937b119d2e172ff",
+        "vs-87e3a157c4bf4020c3e2",
+        "vs-888daac8864ea97b888b",
+        "vs-8c065834d1dd7b31ed06",
+        "vs-91611a5ea572fe44eb85",
+        "vs-968da552067676e21156",
+        "vs-9b37c8c280e1f8fb2931",
+        "vs-9bdf22f48d87e53232d3",
+        "vs-a08267e708a0d062deda",
+        "vs-a1b8c129c1c802a9c4df",
+        "vs-a2ac40a292ff2188faad",
+        "vs-a43661449cc1260af65e",
+        "vs-aa677af08dcffd8c7850",
+        "vs-b527dbdd1b2a42673279",
+        "vs-b7b4e3c69344f5e15aeb",
+        "vs-bb8c7c5a7d11eb12af62",
+        "vs-bddb8426bd3209b2ce3d",
+        "vs-be64d48a1087d11a583e",
+        "vs-be910d106122a205eb3c",
+        "vs-c3084b79e88b358909ef",
+        "vs-c335a6a4646d6cd4361d",
+        "vs-c41279c9ae0ed7625836",
+        "vs-c53e2d3a93e877758a73",
+        "vs-c58ab96ccce88ffc79a0",
+        "vs-c7f922bdfcc208735ab6",
+        "vs-c9c578b99f61b51e35c1",
+        "vs-c9f8c3785afd8dc1362f",
+        "vs-cfaf9657408df6c783cb",
+        "vs-d10aa2de8f64e530fedc",
+        "vs-d17283e1f0838c113fc3",
+        "vs-d1e040e5472107601330",
+        "vs-d4e59c99308f14e2d4e9",
+        "vs-d7920cd092fe7976a479",
+        "vs-d858a4c6917abc83544b",
+        "vs-de203e6a56331f4af8ab",
+        "vs-e11c24da1bfa8b1ae9ed",
+        "vs-e9dc7a1354bf81e76fc9",
+        "vs-ee58e7f1a9f9b0a75943",
+        "vs-ee7397fc6a7042647326",
+        "vs-f23dc54f2b5c9b1fd1dc",
+        "vs-f24accf9e5e6aeef9dc7",
+        "vs-f44a63b6850fb59825ea",
+        "vs-f4aca4963551e029e128",
+        "vs-f9a131a02a95831aeec5",
+        "vs-fac24ed2aec335a1e7aa",
+        "vs-facff9964e42ac52a42d",
+        "vs-fb4aa1bcac10abea1c04",
+        "vs-fd8a3bf76b544db6f709",
+        "vs-fd965193aee5960e898d",
+        "vs-fe8058124eefda2a2de5",
+        "vs-ff960d306f71c56d677b"
+      ],
       "validation": [
         "vs-008aff2c83f8804bbe90",
         "vs-01984005c93ea05a5b8f",
@@ -4063,26 +6369,26 @@
     },
     "discarded_source_rows": {
       "test": 89,
+      "train": 114,
       "validation": 89
     },
-    "evaluation_row_unit": "100-sentence chunk",
     "minimum_validation_and_test_source_rows_per_author": 100,
     "mixed_work_chunks": {},
     "represented_source_rows": {
       "test": 1400,
-      "train": 11914,
+      "train": 11800,
       "validation": 1400
     },
     "retained_authors": [
       "Homeric-Iliad",
       "Homeric-Odyssey"
     ],
+    "row_unit": "100-sentence chunk",
     "rows": {
       "test": 14,
-      "train": 11914,
+      "train": 118,
       "validation": 14
     },
-    "shared_source_selection_target": 100,
-    "train_row_unit": "sentence"
+    "shared_source_selection_target": 100
   }
 }

scripts/build_dataset.py

@@ -1545,10 +1545,7 @@ def validate(rows_by_config: dict[str, list[dict]]) -> dict:
         suffix = config.rsplit("_", 1)[1]
         if suffix != "1":
             target = int(suffix)
-            assert all(
-                row["chunk_size"] == (1 if row["split"] == "train" else target)
-                for row in rows
-            )
+            assert all(row["chunk_size"] == target for row in rows)
             assert all(row["chunk_target_size"] == target for row in rows)
             assert all(len(row["constituent_ids"]) == row["chunk_size"] for row in rows)
         if base_config == "prose":
@@ -1662,6 +1659,7 @@ def validate(rows_by_config: dict[str, list[dict]]) -> dict:
         "complete_chunk_text_and_syllable_aggregation": True,
         "scansion_column_absent": True,
         "fixed_exact_evaluation_chunks": True,
+        "fixed_exact_chunks_all_splits": True,
         "official_conll18_loader": True,
         "unique_conllu_documents_checked": len(checked_conllu),
         "identifier_free_conllu_comments": True,
@@ -1785,10 +1783,10 @@ def main() -> None:
             "strategy": "independent row-level 80/10/10 stratification by author and work",
             "variants": {
                 "_1": "shared 100-task-eligible atomic rows",
-                "_10": "shared atomic training rows; fixed exact 10-row evaluation chunks",
-                "_100": "shared atomic training rows; fixed exact 100-row evaluation chunks",
+                "_10": "fixed exact 10-row chunks in every split",
+                "_100": "fixed exact 100-row chunks in every split",
             },
-            "evaluation_chunking": {
+            "chunking": {
                 "seed": RANDOM_SEED,
                 "remainder_policy": (
                     "discard n modulo 100 rows per author and split by stable hash once; "
@@ -1799,7 +1797,7 @@ def main() -> None:
                     "emit complete single-work chunks first, then combine residual work tails"
                 ),
                 "training_policy": (
-                    "retain 100-task-eligible-author training rows as atomic units in all tasks"
+                    "chunk training identically to validation and test; augmentation is model-side"
                 ),
             },
         },

scripts/dataset_variants.py

@@ -106,12 +106,12 @@ def select_bottleneck_rows(
 ) -> tuple[list[dict], set[str], dict[str, list[str]]]:
     """Select the atomic corpus shared by every task size for one genre."""
     retained_authors = eligible_authors(rows, BOTTLENECK_TARGET)
-    discarded_by_split: dict[str, list[str]] = {"validation": [], "test": []}
+    discarded_by_split: dict[str, list[str]] = {split: [] for split in SPLITS}
     discarded_ids = {
         row["id"] for row in rows if row["author"] not in retained_authors
     }
 
-    for split in ("validation", "test"):
+    for split in SPLITS:
         by_author = defaultdict(list)
         for row in rows:
             if row["split"] == split and row["author"] in retained_authors:
@@ -147,24 +147,6 @@ def _provenance(row: dict) -> dict:
     return {key: row.get(key) for key in keys if key in row}
 
 
-def _chunk_metadata(row: dict, target: int) -> dict:
-    row = dict(row)
-    row.update({
-        "text": encode_text_units(source_text_units(row["text"])),
-        "conllu": encode_conllu_units(source_conllu_units(row["conllu"])),
-        "chunk_size": 1,
-        "chunk_target_size": target,
-        "constituent_ids": [row["id"]],
-        "constituent_provenance": json.dumps(
-            [_provenance(row)], ensure_ascii=False, sort_keys=True,
-        ),
-        "chunk_work_ids": [row["work_id"]],
-        "chunk_works": [row["work"]],
-        "chunk_is_mixed_work": False,
-    })
-    return row
-
-
 def _merge_json_records(rows: list[dict], field: str) -> str:
     merged = []
     seen = set()
@@ -276,7 +258,7 @@ def chunk_author_rows(
     split: str,
 ) -> tuple[list[dict], list[str]]:
     """Discard the seeded remainder and maximize single-work chunks."""
-    assert split in {"validation", "test"}
+    assert split in SPLITS
     discarded_ids = seeded_discard_ids(base_config, rows, target, split)
     discarded = set(discarded_ids)
 
@@ -327,12 +309,8 @@ def make_dataset_variants(
         }
         for target in CHUNK_TARGETS:
             config = f"{base_config}_{target}"
-            variant_rows = [
-                _chunk_metadata(row, target)
-                for row in shared_rows
-                if row["split"] == "train"
-            ]
-            for split in ("validation", "test"):
+            variant_rows = []
+            for split in SPLITS:
                 split_chunks = []
                 by_author = defaultdict(list)
                 for row in shared_rows:
@@ -359,8 +337,7 @@ def make_dataset_variants(
                 "minimum_validation_and_test_source_rows_per_author": BOTTLENECK_TARGET,
                 "retained_authors": sorted(retained_authors),
                 "shared_source_selection_target": BOTTLENECK_TARGET,
-                "train_row_unit": "line" if base_config == "verse_metre" else "sentence",
-                "evaluation_row_unit": f"{target}-" + (
+                "row_unit": f"{target}-" + (
                     "line chunk" if base_config == "verse_metre" else "sentence chunk"
                 ),
                 "rows": dict(split_rows),

scripts/validate_dataset.py

@@ -299,11 +299,10 @@ def validate_variants(configs: dict[str, list[dict]]) -> None:
         atomic_authors = {row["author"] for row in atomic}
         atomic_counts = {
             split: Counter(row["author"] for row in atomic if row["split"] == split)
-            for split in ("validation", "test")
+            for split in SPLITS
         }
-        assert set(atomic_counts["validation"]) == atomic_authors
-        assert set(atomic_counts["test"]) == atomic_authors
-        for split in ("validation", "test"):
+        for split in SPLITS:
+            assert set(atomic_counts[split]) == atomic_authors
             assert all(
                 count >= BOTTLENECK_TARGET and count % BOTTLENECK_TARGET == 0
                 for count in atomic_counts[split].values()
@@ -313,16 +312,7 @@ def validate_variants(configs: dict[str, list[dict]]) -> None:
             config = f"{base}_{threshold}"
             rows = configs[config]
             assert {row["author"] for row in rows} == atomic_authors
-            train = [row for row in rows if row["split"] == "train"]
-            expected_train_ids = {
-                row["id"] for row in atomic if row["split"] == "train"
-            }
-            assert {row["id"] for row in train} == expected_train_ids
-            assert all(row["chunk_size"] == 1 for row in train)
-            assert all(row["chunk_target_size"] == threshold for row in train)
-            assert all(row["constituent_ids"] == [row["id"]] for row in train)
-
-            for split in ("validation", "test"):
+            for split in SPLITS:
                 chunks = [row for row in rows if row["split"] == split]
                 by_author = {}
                 for row in atomic:

tests/test_split_stratification.py

@@ -18,7 +18,7 @@ DATA_ROOT = Path(__file__).resolve().parents[1] / "data"
 BASE_CONFIGS = ("prose", "verse_sentence", "verse_metre")
 
 
-def test_atomic_splits_are_balanced_and_100_chunkable() -> None:
+def test_all_atomic_splits_are_balanced_and_100_chunkable() -> None:
     for base_config in BASE_CONFIGS:
         config = f"{base_config}_1"
         counts = defaultdict(Counter)
@@ -31,8 +31,9 @@ def test_atomic_splits_are_balanced_and_100_chunkable() -> None:
         for author, author_counts in counts.items():
             assert author_counts["train"] > 0, (config, author)
             assert author_counts["validation"] == author_counts["test"], (config, author)
-            assert author_counts["validation"] >= 100, (config, author)
-            assert author_counts["validation"] % 100 == 0, (config, author)
+            for split in ("train", "validation", "test"):
+                assert author_counts[split] >= 100, (config, author, split)
+                assert author_counts[split] % 100 == 0, (config, author, split)
 
 
 def test_each_genre_retains_approximately_balanced_source_splits() -> None:
@@ -49,14 +50,11 @@ def test_each_genre_retains_approximately_balanced_source_splits() -> None:
         assert 0.78 <= counts["train"] / total <= 0.83, (config, counts)
 
 
-def test_chunkable_variants_have_atomic_train_and_exact_evaluation_chunks() -> None:
+def test_chunkable_variants_have_exact_chunks_in_every_split() -> None:
     for base_config in BASE_CONFIGS:
         for threshold in (10, 100):
             config = f"{base_config}_{threshold}"
-            train_path = DATA_ROOT / config / "train-00000-of-00001.parquet"
-            train_sizes = pq.read_table(train_path, columns=["chunk_size"])["chunk_size"].to_pylist()
-            assert train_sizes and set(train_sizes) == {1}
-            for split in ("validation", "test"):
+            for split in ("train", "validation", "test"):
                 path = DATA_ROOT / config / f"{split}-00000-of-00001.parquet"
                 table = pq.read_table(
                     path, columns=["author", "chunk_size", "constituent_ids"],
@@ -97,15 +95,11 @@ def test_all_task_sizes_cover_exactly_the_same_atomic_rows() -> None:
                     split,
                 )
                 for chunk in chunks:
-                    expected_text = (
-                        atomic_rows[split][chunk["constituent_ids"][0]]
-                        if split == "train"
-                        else [
-                            unit
-                            for row_id in chunk["constituent_ids"]
-                            for unit in atomic_rows[split][row_id]
-                        ]
-                    )
+                    expected_text = [
+                        unit
+                        for row_id in chunk["constituent_ids"]
+                        for unit in atomic_rows[split][row_id]
+                    ]
                     assert load_text_units(chunk["text"]) == expected_text