build_info.json

{
 "ambig": 54,
 "amp": "bf16",
 "anger_balanced_per_class_split": "144 / 18 / 18",
 "anger_split_balanced_split": "288 / 36 / 36",
 "audit_agree_new": 4,
 "audit_agree_prev": 18,
 "audit_caveat": "one reader, unblinded to the hypothesis, and the pool is short, shouty, code-mixed Indonesian - rerun this on a larger sample before quoting it",
 "audit_n": 37,
 "audit_note": "A sample of 37 rows where the readings disagree was judged against the operational boundary by one reader working from the tweet text alone, before seeing any probability: 18/37 of those judgements land on the two-language reading, 4/37 on the label here, and 13/37 read as neither emotion. Of the 20 rows labelled `contempt` here and `anger` by the two-language reading, 1 was accepted as contempt. The reader is a machine reader, not a human annotator, and works on short code-mixed text - a signal, not gold labels.",
 "audit_other": 13,
 "balanced_b": 126,
 "balanced_per_class_split": "144 / 18 / 18",
 "balanced_split": "1008 / 126 / 126",
 "budget": 6144,
 "checkpoint": "convaiinnovations/laya (subfolder `multilingual/`)",
 "choice_n": 421,
 "class_table": "| `anger` | 289 | 12.9% | split here: laya on the `anger` pool |\n| `contempt` | 186 | 8.3% | split here: laya on the `anger` pool |\n| `disgust` | 355 | 15.8% | EmoTweetID annotators, kept verbatim |\n| `enjoyment` | 429 | 19.1% | EmoTweetID annotators, kept verbatim |\n| `fear` | 395 | 17.6% | EmoTweetID annotators, kept verbatim |\n| `sadness` | 303 | 13.5% | EmoTweetID annotators, kept verbatim |\n| `surprise` | 286 | 12.8% | EmoTweetID annotators, kept verbatim |",
 "conf_low_new": 99,
 "conf_low_prev": 48,
 "conf_note": "99 of 475 rows land within 0.10 of a coin flip on the primary reading (max probability of the two options under 0.60), and the mean max probability across the pool is 0.754",
 "ctx_default": 1024,
 "datasets_v": "5.0.1",
 "dup_conflicts": 14,
 "duplicates_note": "29 rows share an identical `text` string with another row (more, if you count pairs whose English translation collides), and 14 of those repeated wordings repeat with *different* upstream labels - the annotators disagreed, and this dataset inherits that rather than re-judging it. Splitting is therefore **group-aware**: every member of a duplicate group lands in one split, so no wording appears in either side of a train/valid/test boundary (`verify()` fails the build if one does). The balanced configs sample whole groups as well, so a dropped row never orphans its duplicate.",
 "enc_name": "jhu-clsp/mmBERT-base",
 "flip_to_anger": 12,
 "flip_to_contempt": 104,
 "flipped": 116,
 "head_max_len": 144,
 "kept_n": 3,
 "laya_seconds": 851.2,
 "laya_v": "0.3.4",
 "lexicon_contempt_new": 141,
 "lexicon_contempt_prev": 57,
 "lexicon_note": "373 of the 475 pool rows contain an explicit Indonesian anger word (`kesal`, `marah`, `murka`, `benci`, `jengkel`, `geram`, `tersinggung`, `muak`, `ngamuk`) - which is how EmoTweetID's annotators sampled, so the word is upstream evidence for anger. 141 of those 373 rows (38%) are labelled `contempt` here.",
 "lexicon_rows": 373,
 "lowconf": 391,
 "lowconf_laya": 391,
 "margin": 0.1,
 "max_len": 256,
 "mean_p_anger_prompt": 0.589,
 "mean_p_anger_swapped": 0.726,
 "model_type": "modernbert",
 "n_anger_balanced": 360,
 "n_anger_balanced_s": "360",
 "n_anger_kept": 289,
 "n_anger_pool": "475",
 "n_anger_pool_rows": 475,
 "n_balanced": 1260,
 "n_balanced_s": "1,260",
 "n_contempt": 186,
 "n_full_pool": 2243,
 "n_manual": "1,768",
 "n_pool": "2,243",
 "naive_rows": 1900,
 "noul_n": 33,
 "offtopic": 18,
 "order_flip": 25.1,
 "order_note": "Listing contempt first moved the argmax on 25.1% of the pool (mean |delta p(anger)| 0.191; mean P(anger) 0.589 as prompted vs 0.726 with the options swapped), so the prompt's option order is worth roughly a third of the contempt shift",
 "order_shift": 0.191,
 "orderavg_n": 18,
 "params": "321.9M",
 "params_enc": "306.9M",
 "params_heads": "15.0M",
 "per_class_anger_balanced": 180,
 "per_class_balanced": 180,
 "prev_anger": 381,
 "prev_contempt": 94,
 "prev_note": "it labelled 381 anger / 94 contempt; the labels here differ on 116 of the 475 rows, 104 of them anger -> contempt",
 "prev_share_contempt": 19.8,
 "probe_author": "13/16 (0.812)",
 "probe_en": "14/16 (0.875)",
 "probe_en_idq": "14/16 (0.875)",
 "probe_en_q": "English items with the Indonesian question: 14/16 (0.875)",
 "probe_gap": "The gap is 3 items out of 16: the same sentences are called correctly 14 times in English and 11 times in Indonesian",
 "probe_id": "11/16 (0.688)",
 "probe_id_idq": "12/16 (0.750)",
 "probe_id_swapped": "11/16 (0.688)",
 "probe_lang_q": "with the question in Indonesian instead of English, the gap closes only 11 -> 12 items",
 "probe_mean_p": "Mean P(anger) on the anger probes 0.68 in Indonesian vs 0.80 in English; on the contempt probes 0.31 vs 0.20",
 "probe_order": "On the contempt items the mean P(anger) is 0.31 with anger listed first and 0.48 with contempt listed first - the order effect seen on the corpus",
 "reconfirm_note": "every stage (id_core, id_core_swap, id_diag, id_veto) matched row for row and reproduced **475/475 labels**, down to a byte-identical `anger_ekman_rows.csv`, the largest probability difference anywhere being 0.000000 (856.6 s of model time against 851.2 s for the shipped run)",
 "repo": "mahalisyarifuddin/emotweetid-ekman7",
 "repro_note": "the `id_core` stage matches the earlier two-language run's `out_prev/cache_id_core.json` (shipped here) to the last digit - 471/471 unique texts on the same side of the decision line, mean |delta p(anger)| 0.0000, max 0.0000",
 "scored_rows": 1533,
 "share_contempt": 39.2,
 "sources_str": "`ekman_choice_id` 421, `ekman_noul_tiebreak` 33, `ekman_choice_order_avg` 18, `kept_original_label` 3",
 "split_exact_note": "balanced 1008 / 126 / 126 of 1,260 (b=126), full 1 split of 2,243, anger_split 1 split of 475, anger_split_balanced 288 / 36 / 36 of 360 (b=36)",
 "split_table": "| `balanced` | train | 1,008 | anger 144 / contempt 144 / disgust 144 / enjoyment 144 / fear 144 / sadness 144 / surprise 144 |\n| `balanced` | valid | 126 | anger 18 / contempt 18 / disgust 18 / enjoyment 18 / fear 18 / sadness 18 / surprise 18 |\n| `balanced` | test | 126 | anger 18 / contempt 18 / disgust 18 / enjoyment 18 / fear 18 / sadness 18 / surprise 18 |\n| `full` | train | 2,243 | anger 289 / contempt 186 / disgust 355 / enjoyment 429 / fear 395 / sadness 303 / surprise 286 (the whole pool, one split) |\n| `anger_split` | train | 475 | anger 289 / contempt 186 (the whole pool, one split) |\n| `anger_split_balanced` | train | 288 | anger 144 / contempt 144 |\n| `anger_split_balanced` | valid | 36 | anger 18 / contempt 18 |\n| `anger_split_balanced` | test | 36 | anger 18 / contempt 18 |",
 "splits": {
  "anger_split": {
   "train": 475
  },
  "anger_split_balanced": {
   "test": 36,
   "train": 288,
   "valid": 36
  },
  "balanced": {
   "test": 126,
   "train": 1008,
   "valid": 126
  },
  "full": {
   "train": 2243
  }
 },
 "stageC": 54,
 "stage_diag": 108,
 "stages": "id core 299.8 s, id core swap 299.5 s, id diag 53.5 s, id veto 198.4 s",
 "tensors": 170,
 "torch_v": "2.14.0+cpu",
 "transformers_v": "5.17.0",
 "veto": "18 of 475 rows above 0.5 (mean P(neither) 0.12) - Indonesian reading",
 "vocab": 256000,
 "whole_note": "`full` and `anger_split` are the complete pools in a single `train` split - nothing is held out, and users who want their own validation split take it from there. The two balanced configs are the ones that carry an exact 8:1:1 train/valid/test."
}