SlowGuess/NAACL_2022_OCR huggingface dataset:
Table 13: Mean precision at one (P@1) of various models on LAMA-UHN probe. Best results are highlighted in bold and the second best performance is underlined
Complete	Google-RE	T-REx
DoB	PoB	PoD	All	1-1	N-1	N-M	All
BERT-based models
BERT-base	18.72	1.59	6.98	3.98	4.18	62.86	21.99	17.32	22.16
BERT-large	19.92	1.59	7.71	5.66	49.86	70.13	22.35	19.62	23.62
ERNIE	15.81	1.42	6.57	1.38	3.12	55.68	17.90	15.40	18.76
LM-CORE(b,y)	41.33	64.44	46.80	46.02	52.42	71.04	44.42	29.11	39.75
LM-CORE(b,w)	45.50	0.66	30.93	23.39	18.33	80.05	55.16	41.17	50.92
RoBERTa-based models
RoBERTa	13.66	1.85	4.18	0.55	2.19	53.36	13.99	15.80	16.62
RoBERTa-large	17.99	1.41	5.68	0.36	2.48	67.27	20.47	17.86	21.74
KEPLER	12.46	1.47	4.88	0.91	2.42	48.70	12.60	14.44	15.08
CoLAKE	17.16	1.79	6.89	5.83	4.84	59.84	18.85	17.61	20.37
LM-CORE(r,y)	34.25	46.33	35.66	19.31	33.77	63.83	37.74	25.20	34.12
LM-CORE(r,w)	44.75	0.38	24.80	20.04	15.07	67.53	55.72	39.77	50.07