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mit_restaurant
quynong/mit_restaurant
8
[ "Rating", "Amenity", "Location", "Restaurant_Name", "Price", "Hours", "Dish", "Cuisine" ]
test
[ "corenlp", "deberta-v3-base", "distilbert-base-cased" ]
[ 42, 43, 44 ]
5
16
null
null
{ "corenlp": { "status": "ok", "name": "corenlp", "seed": null, "repo": "AITeamUIT/baseline-corenlp-mit_restaurant", "runtime_sec": 303.7, "recovered_from": "train_eval", "summary": { "micro_precision": 0.8717, "micro_recall": 0.8283, "micro_f1": 0.8495, "macro_prec...
[ { "baseline": "corenlp", "n": 0, "micro_f1_mean": 84.95, "micro_f1_std": null, "macro_f1_mean": 83.67, "macro_f1_std": null, "micro_p_mean": 87.17, "micro_r_mean": 82.83, "micro_f1": "84.95", "macro_f1": "83.67" }, { "baseline": "deberta-v3-base", "n": 3, "mic...
2026-08-24T17:41:33

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Check out the documentation for more information.

mit_restaurant — NER baselines (Stanford CoreNLP CRF, DeBERTa-v3-base, DistilBERT-base)

  • dataset: quynong/mit_restaurant (8 nhan), eval tren split test
  • HF baselines: 5 epochs, batch 16, seeds [42, 43, 44]
  • corenlp: 1 lan train (CRF deterministic, khong seed)

Ket qua (P/R/F1 %, HungarianEvaluator IoU>=0.5 hoac fuzzy)

baseline n micro F1 macro F1 micro P micro R
corenlp 0 84.95 83.67 87.17 82.83
deberta-v3-base 3 88.39 +/- 0.56 87.49 +/- 0.84 86.75 90.1
distilbert-base-cased 3 86.54 +/- 0.26 85.54 +/- 0.34 84.55 88.64

Checkpoint tung run

generated 2026-08-24T17:41:33

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