sembr2023-bert-mini

admko/sembr2023-bert-mini

sembr2023-bert-mini is a token classification model from admko. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Token Classification
Library: transformers
License: mit
Model type: bert
Access: Public
Created: Nov 27, 2023
Updated: May 30, 2026
SHA: 25fd5ba9

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Base models

prajjwal1/bert-mini

Model card

From the Hugging Face model README

sembr2023-bert-mini

This model is a fine-tuned version of prajjwal1/bert-mini on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.1844
Precision: 0.7925
Recall: 0.7950
F1: 0.7938
Iou: 0.6581
Accuracy: 0.9620
Balanced Accuracy: 0.8870
Overall Accuracy: 0.9443

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 64
eval_batch_size: 128
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
training_steps: 1000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Iou	Accuracy	Balanced Accuracy	Overall Accuracy
0.5028	0.06	10	0.5036	0	0.0	0.0	0.0	0.9080	0.5	0.9080
0.417	0.12	20	0.4416	0	0.0	0.0	0.0	0.9080	0.5	0.9080
0.4292	0.18	30	0.4298	0	0.0	0.0	0.0	0.9080	0.5	0.9080
0.3964	0.24	40	0.4121	0	0.0	0.0	0.0	0.9080	0.5	0.9080
0.3611	0.3	50	0.3619	0.5833	0.0080	0.0158	0.0080	0.9082	0.5037	0.9081
0.3201	0.36	60	0.3103	0.6414	0.5085	0.5673	0.3960	0.9287	0.7399	0.9146
0.2983	0.42	70	0.2575	0.7866	0.6423	0.7072	0.5470	0.9511	0.8123	0.9333
0.2621	0.48	80	0.2402	0.7879	0.6824	0.7314	0.5765	0.9539	0.8319	0.9354
0.2464	0.55	90	0.2262	0.8304	0.6660	0.7391	0.5862	0.9568	0.8261	0.9384
0.21	0.61	100	0.2186	0.7990	0.7131	0.7536	0.6047	0.9571	0.8475	0.9382
0.2238	0.67	110	0.2042	0.8242	0.6860	0.7487	0.5984	0.9577	0.8356	0.9408
0.1849	0.73	120	0.1990	0.8799	0.6422	0.7425	0.5904	0.9590	0.8166	0.9432
0.1702	0.79	130	0.1959	0.7862	0.7436	0.7643	0.6185	0.9578	0.8615	0.9392
0.2033	0.85	140	0.1939	0.7996	0.7414	0.7694	0.6252	0.9591	0.8613	0.9404
0.1686	0.91	150	0.1916	0.7982	0.7437	0.7700	0.6260	0.9591	0.8623	0.9410
0.1484	0.97	160	0.1855	0.7898	0.7597	0.7745	0.6320	0.9593	0.8696	0.9408
0.1717	1.03	170	0.1864	0.8231	0.7301	0.7738	0.6310	0.9607	0.8571	0.9434
0.1584	1.09	180	0.1844	0.7919	0.7676	0.7796	0.6388	0.9601	0.8736	0.9414
0.1455	1.15	190	0.1807	0.8255	0.7496	0.7857	0.6470	0.9624	0.8668	0.9437
0.1521	1.21	200	0.1746	0.8277	0.7428	0.7829	0.6433	0.9621	0.8636	0.9450
0.1385	1.27	210	0.1833	0.8099	0.7573	0.7828	0.6431	0.9613	0.8697	0.9430
0.1246	1.33	220	0.1857	0.7400	0.8164	0.7763	0.6344	0.9567	0.8937	0.9366
0.1312	1.39	230	0.1738	0.8345	0.7441	0.7867	0.6484	0.9629	0.8646	0.9458
0.1139	1.45	240	0.1750	0.7964	0.7861	0.7912	0.6545	0.9618	0.8829	0.9428
0.1181	1.52	250	0.1728	0.8201	0.7578	0.7877	0.6498	0.9624	0.8705	0.9448
0.1248	1.58	260	0.1774	0.8148	0.7727	0.7932	0.6573	0.9629	0.8775	0.9441
0.1384	1.64	270	0.1748	0.7840	0.7925	0.7882	0.6505	0.9608	0.8852	0.9418
0.1068	1.7	280	0.1744	0.7943	0.7950	0.7947	0.6593	0.9622	0.8871	0.9430
0.114	1.76	290	0.1749	0.7916	0.7904	0.7910	0.6543	0.9616	0.8847	0.9428
0.1214	1.82	300	0.1778	0.7551	0.8233	0.7877	0.6498	0.9592	0.8981	0.9392
0.1139	1.88	310	0.1764	0.7897	0.7839	0.7868	0.6485	0.9609	0.8814	0.9425
0.1254	1.94	320	0.1771	0.7908	0.7925	0.7916	0.6551	0.9616	0.8856	0.9427
0.1001	2.0	330	0.1715	0.8057	0.7829	0.7942	0.6586	0.9627	0.8819	0.9445
0.0989	2.06	340	0.1705	0.8099	0.7803	0.7948	0.6595	0.9629	0.8809	0.9446
0.1222	2.12	350	0.1761	0.7843	0.7991	0.7916	0.6551	0.9613	0.8884	0.9422
0.1032	2.18	360	0.1754	0.7961	0.7864	0.7912	0.6545	0.9618	0.8830	0.9432
0.0799	2.24	370	0.1753	0.7867	0.7879	0.7873	0.6492	0.9608	0.8831	0.9432
0.099	2.3	380	0.1751	0.8101	0.7738	0.7915	0.6549	0.9625	0.8777	0.9451
0.0993	2.36	390	0.1699	0.8073	0.7791	0.7929	0.6569	0.9626	0.8801	0.9454
0.1025	2.42	400	0.1662	0.8203	0.7764	0.7978	0.6636	0.9638	0.8796	0.9465
0.1081	2.48	410	0.1762	0.8005	0.7893	0.7949	0.6596	0.9625	0.8847	0.9444
0.1118	2.55	420	0.1720	0.8130	0.7755	0.7938	0.6582	0.9630	0.8787	0.9458
0.0779	2.61	430	0.1712	0.8131	0.7797	0.7961	0.6612	0.9633	0.8808	0.9454
0.0944	2.67	440	0.1788	0.7754	0.8094	0.7921	0.6557	0.9609	0.8928	0.9419
0.1053	2.73	450	0.1696	0.7980	0.7901	0.7940	0.6584	0.9623	0.8849	0.9450
0.0889	2.79	460	0.1719	0.8215	0.7736	0.7968	0.6623	0.9637	0.8783	0.9465
0.0879	2.85	470	0.1712	0.8091	0.7828	0.7957	0.6608	0.9630	0.8820	0.9457
0.0867	2.91	480	0.1769	0.8021	0.78	0.7909	0.6541	0.9621	0.8803	0.9447
0.0787	2.97	490	0.1788	0.8044	0.7831	0.7936	0.6578	0.9625	0.8819	0.9447
0.0945	3.03	500	0.1736	0.8055	0.7820	0.7936	0.6578	0.9626	0.8815	0.9445
0.1011	3.09	510	0.1823	0.7881	0.7962	0.7921	0.6558	0.9616	0.8873	0.9432
0.0914	3.15	520	0.1819	0.7958	0.7939	0.7948	0.6595	0.9623	0.8866	0.9438
0.0837	3.21	530	0.1738	0.8129	0.7857	0.7991	0.6654	0.9637	0.8837	0.9460
0.0776	3.27	540	0.1828	0.7921	0.7961	0.7941	0.6585	0.9620	0.8874	0.9437
0.0916	3.33	550	0.1776	0.7835	0.7994	0.7913	0.6547	0.9612	0.8885	0.9433
0.081	3.39	560	0.1784	0.7784	0.8033	0.7907	0.6538	0.9609	0.8901	0.9428
0.0867	3.45	570	0.1793	0.7728	0.8074	0.7897	0.6525	0.9605	0.8917	0.9425
0.0816	3.52	580	0.1789	0.7829	0.8017	0.7922	0.6559	0.9613	0.8896	0.9433
0.0808	3.58	590	0.1791	0.7890	0.7941	0.7916	0.6550	0.9615	0.8863	0.9435
0.07	3.64	600	0.1844	0.7697	0.8071	0.7880	0.6501	0.9600	0.8913	0.9420
0.0775	3.7	610	0.1795	0.7849	0.7957	0.7902	0.6532	0.9612	0.8868	0.9433
0.0722	3.76	620	0.1772	0.7993	0.7814	0.7903	0.6532	0.9619	0.8808	0.9449
0.0786	3.82	630	0.1775	0.8159	0.7763	0.7956	0.6606	0.9633	0.8793	0.9457
0.0768	3.88	640	0.1823	0.8015	0.7848	0.7931	0.6571	0.9623	0.8826	0.9442
0.0728	3.94	650	0.1806	0.7918	0.7885	0.7901	0.6531	0.9615	0.8838	0.9438
0.0762	4.0	660	0.1831	0.7881	0.7935	0.7908	0.6540	0.9614	0.8859	0.9435
0.0776	4.06	670	0.1788	0.8015	0.7847	0.7930	0.6570	0.9623	0.8825	0.9453
0.0843	4.12	680	0.1824	0.8009	0.7876	0.7942	0.6587	0.9625	0.8839	0.9445
0.066	4.18	690	0.1843	0.7921	0.7918	0.7920	0.6556	0.9617	0.8854	0.9440
0.0832	4.24	700	0.1781	0.7957	0.7893	0.7925	0.6563	0.9620	0.8844	0.9447
0.0761	4.3	710	0.1871	0.7817	0.8017	0.7916	0.6550	0.9612	0.8895	0.9428
0.0696	4.36	720	0.1813	0.7957	0.7924	0.7940	0.6584	0.9622	0.8859	0.9446
0.0734	4.42	730	0.1827	0.7934	0.7938	0.7936	0.6578	0.9620	0.8864	0.9444
0.0823	4.48	740	0.1856	0.7956	0.7913	0.7935	0.6576	0.9621	0.8854	0.9443
0.0662	4.55	750	0.1790	0.7890	0.7952	0.7921	0.6557	0.9616	0.8868	0.9444
0.0775	4.61	760	0.1858	0.7899	0.7953	0.7926	0.6564	0.9617	0.8869	0.9439
0.0764	4.67	770	0.1853	0.7852	0.8011	0.7931	0.6572	0.9616	0.8895	0.9436
0.0689	4.73	780	0.1804	0.7964	0.7924	0.7944	0.6589	0.9623	0.8859	0.9445
0.0785	4.79	790	0.1817	0.7937	0.7921	0.7929	0.6569	0.9619	0.8856	0.9444
0.075	4.85	800	0.1856	0.7912	0.7929	0.7920	0.6556	0.9617	0.8858	0.9440
0.0691	4.91	810	0.1844	0.7805	0.8	0.7901	0.6531	0.9609	0.8886	0.9432
0.0835	4.97	820	0.1829	0.7984	0.7911	0.7947	0.6594	0.9624	0.8854	0.9448
0.0712	5.03	830	0.1820	0.7906	0.7927	0.7917	0.6552	0.9616	0.8857	0.9443
0.0594	5.09	840	0.1841	0.7902	0.7948	0.7925	0.6563	0.9617	0.8867	0.9441
0.0775	5.15	850	0.1834	0.7927	0.7936	0.7932	0.6572	0.9619	0.8863	0.9444
0.0755	5.21	860	0.1833	0.7924	0.7944	0.7934	0.6575	0.9619	0.8867	0.9444
0.0717	5.27	870	0.1838	0.7902	0.7958	0.7930	0.6570	0.9618	0.8872	0.9443
0.0694	5.33	880	0.1834	0.7918	0.7939	0.7928	0.6568	0.9618	0.8864	0.9444
0.0759	5.39	890	0.1826	0.7905	0.7954	0.7929	0.6569	0.9618	0.8870	0.9443
0.0666	5.45	900	0.1821	0.7945	0.7922	0.7933	0.6575	0.9620	0.8857	0.9446
0.08	5.52	910	0.1829	0.7924	0.7953	0.7938	0.6581	0.9620	0.8871	0.9444
0.0816	5.58	920	0.1837	0.7918	0.7954	0.7936	0.6578	0.9619	0.8871	0.9443
0.0762	5.64	930	0.1837	0.7922	0.7954	0.7938	0.6581	0.9620	0.8871	0.9443
0.0655	5.7	940	0.1843	0.7906	0.7962	0.7934	0.6575	0.9619	0.8874	0.9442
0.0737	5.76	950	0.1846	0.7904	0.7964	0.7934	0.6576	0.9619	0.8875	0.9441
0.0717	5.82	960	0.1846	0.7905	0.7961	0.7933	0.6574	0.9618	0.8873	0.9441
0.0829	5.88	970	0.1845	0.7917	0.7954	0.7935	0.6577	0.9619	0.8871	0.9443
0.0766	5.94	980	0.1844	0.7924	0.7952	0.7938	0.6581	0.9620	0.8870	0.9443
0.0704	6.0	990	0.1844	0.7925	0.7950	0.7938	0.6581	0.9620	0.8870	0.9443
0.0755	6.06	1000	0.1844	0.7925	0.7950	0.7938	0.6581	0.9620	0.8870	0.9443

Framework versions

Transformers 4.34.1
Pytorch 2.0.1
Datasets 2.14.6
Tokenizers 0.14.1

Task: Token Classification
Library: transformers
Type: bert
License: mit
Created: Nov 27, 2023
Updated: May 30, 2026