bert-base-uncased-finetuned-math_punctuation-ignore_word_parts

This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

Model description

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The following hyperparameters were used during training:

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F Score	Auc
0.1064	0.64	500	0.1082	0.7558	0.6580	0.6964	0.9086
0.0781	1.27	1000	0.1025	0.7594	0.7226	0.7365	0.9261
0.0757	1.91	1500	0.1001	0.7945	0.6899	0.7302	0.9272
0.0538	2.54	2000	0.1061	0.7689	0.7348	0.7480	0.9298
0.0425	3.18	2500	0.1123	0.7806	0.7361	0.7560	0.9300
0.0377	3.81	3000	0.1159	0.7841	0.7437	0.7610	0.9292
0.0235	4.45	3500	0.1259	0.7786	0.7368	0.7561	0.9276
0.0227	5.08	4000	0.1436	0.7699	0.7448	0.7555	0.9277
0.0159	5.72	4500	0.1466	0.7715	0.7333	0.7514	0.9252
0.0106	6.35	5000	0.1574	0.7710	0.7456	0.7566	0.9276
0.0111	6.99	5500	0.1560	0.7694	0.7500	0.7595	0.9286
0.0074	7.62	6000	0.1645	0.7789	0.7511	0.7639	0.9305
0.0056	8.26	6500	0.1745	0.7887	0.7453	0.7648	0.9265
0.005	8.89	7000	0.1760	0.7779	0.7497	0.7629	0.9281
0.0038	9.53	7500	0.1873	0.7826	0.7505	0.7634	0.9273
0.0031	10.17	8000	0.1896	0.7855	0.7477	0.7644	0.9258
0.0026	10.8	8500	0.1929	0.7849	0.7485	0.7650	0.9263
0.0017	11.44	9000	0.1981	0.7843	0.7485	0.7648	0.9248