sembr2023-bert-small

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

Loss: 0.2324
Precision: 0.7915
Recall: 0.8418
F1: 0.8159
Iou: 0.6890
Accuracy: 0.9651
Balanced Accuracy: 0.9097
Overall Accuracy: 0.9481

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
0.4134	0.06	10	0.4107	0	0.0	0.0	0.0	0.9080	0.5	0.9080
0.371	0.12	20	0.3698	0	0.0	0.0	0.0	0.9080	0.5	0.9080
0.2913	0.18	30	0.2672	0.8443	0.4167	0.5580	0.3870	0.9393	0.7045	0.9283
0.2315	0.24	40	0.2184	0.8043	0.6761	0.7346	0.5806	0.9551	0.8297	0.9364
0.1693	0.3	50	0.2021	0.8064	0.7375	0.7704	0.6265	0.9596	0.8598	0.9396
0.1812	0.36	60	0.1869	0.8727	0.6847	0.7674	0.6225	0.9618	0.8373	0.9437
0.1745	0.42	70	0.1855	0.8021	0.7744	0.7880	0.6502	0.9617	0.8775	0.9421
0.1577	0.48	80	0.1817	0.8207	0.7641	0.7914	0.6548	0.9630	0.8736	0.9431
0.1458	0.55	90	0.1763	0.8183	0.7869	0.8023	0.6698	0.9643	0.8846	0.9449
0.1343	0.61	100	0.1772	0.8721	0.7372	0.7990	0.6652	0.9659	0.8631	0.9477
0.1442	0.67	110	0.1647	0.8388	0.7795	0.8081	0.6779	0.9659	0.8822	0.9483
0.1104	0.73	120	0.1678	0.8488	0.7679	0.8063	0.6755	0.9661	0.8770	0.9479
0.1089	0.79	130	0.1745	0.7882	0.8262	0.8068	0.6761	0.9636	0.9019	0.9434
0.1437	0.85	140	0.1768	0.7970	0.8206	0.8086	0.6787	0.9643	0.8997	0.9440
0.1104	0.91	150	0.1710	0.7961	0.8275	0.8115	0.6828	0.9646	0.9030	0.9446
0.0941	0.97	160	0.1647	0.8007	0.8167	0.8086	0.6787	0.9644	0.8980	0.9456
0.1146	1.03	170	0.1744	0.8026	0.8250	0.8136	0.6858	0.9652	0.9022	0.9456
0.0982	1.09	180	0.1636	0.8175	0.8191	0.8183	0.6925	0.9666	0.9003	0.9468
0.0875	1.15	190	0.1653	0.8305	0.8064	0.8183	0.6924	0.9671	0.8948	0.9476
0.0962	1.21	200	0.1610	0.8340	0.8076	0.8206	0.6958	0.9675	0.8957	0.9490
0.084	1.27	210	0.1671	0.8232	0.8177	0.8204	0.6955	0.9671	0.9000	0.9476
0.07	1.33	220	0.1665	0.7909	0.8545	0.8215	0.6971	0.9658	0.9158	0.9454
0.0785	1.39	230	0.1612	0.8411	0.8004	0.8202	0.6953	0.9677	0.8925	0.9496
0.0712	1.45	240	0.1638	0.8251	0.8161	0.8205	0.6957	0.9672	0.8993	0.9491
0.0683	1.52	250	0.1823	0.8097	0.8262	0.8179	0.6919	0.9662	0.9033	0.9463
0.0694	1.58	260	0.1717	0.8028	0.8408	0.8214	0.6969	0.9664	0.9099	0.9474
0.0809	1.64	270	0.1681	0.8304	0.8102	0.8202	0.6952	0.9673	0.8967	0.9491
0.0586	1.7	280	0.1811	0.8096	0.8391	0.8241	0.7008	0.9671	0.9096	0.9478
0.069	1.76	290	0.1855	0.8088	0.8284	0.8185	0.6928	0.9662	0.9043	0.9478
0.0739	1.82	300	0.1876	0.8148	0.8209	0.8178	0.6918	0.9664	0.9010	0.9476
0.0691	1.88	310	0.1741	0.8173	0.8206	0.8190	0.6934	0.9666	0.9010	0.9485
0.0728	1.94	320	0.1765	0.7941	0.8346	0.8139	0.6861	0.9649	0.9064	0.9469
0.0585	2.0	330	0.1800	0.8118	0.8166	0.8142	0.6866	0.9657	0.8987	0.9483
0.0602	2.06	340	0.1930	0.7969	0.8366	0.8162	0.6895	0.9654	0.9075	0.9461
0.0557	2.12	350	0.1832	0.7915	0.8401	0.8151	0.6879	0.9649	0.9089	0.9472
0.0491	2.18	360	0.1914	0.8131	0.8136	0.8134	0.6854	0.9657	0.8973	0.9489
0.0413	2.24	370	0.2116	0.7989	0.8288	0.8136	0.6857	0.9651	0.9038	0.9463
0.051	2.3	380	0.2073	0.7864	0.8454	0.8148	0.6875	0.9647	0.9111	0.9460
0.0529	2.36	390	0.1923	0.8103	0.8278	0.8190	0.6934	0.9663	0.9041	0.9496
0.0469	2.42	400	0.1808	0.8131	0.8217	0.8173	0.6911	0.9662	0.9013	0.9497
0.0579	2.48	410	0.2053	0.7795	0.8493	0.8129	0.6848	0.9640	0.9125	0.9464
0.0494	2.55	420	0.1953	0.7872	0.8457	0.8154	0.6883	0.9648	0.9113	0.9471
0.0468	2.61	430	0.1972	0.8064	0.8182	0.8123	0.6839	0.9652	0.8992	0.9488
0.0545	2.67	440	0.2116	0.7774	0.8455	0.8100	0.6807	0.9635	0.9105	0.9458
0.0544	2.73	450	0.1954	0.7868	0.8455	0.8151	0.6879	0.9647	0.9111	0.9472
0.044	2.79	460	0.2046	0.8149	0.8203	0.8175	0.6914	0.9663	0.9007	0.9491
0.0468	2.85	470	0.2036	0.8031	0.8321	0.8174	0.6911	0.9658	0.9057	0.9483
0.0457	2.91	480	0.1998	0.7923	0.8377	0.8144	0.6869	0.9649	0.9077	0.9479
0.0435	2.97	490	0.2077	0.7864	0.8432	0.8138	0.6860	0.9645	0.9100	0.9475
0.0489	3.03	500	0.2067	0.7933	0.8339	0.8131	0.6850	0.9647	0.9059	0.9478
0.0472	3.09	510	0.2204	0.7883	0.8464	0.8163	0.6896	0.9650	0.9117	0.9475
0.0469	3.15	520	0.2209	0.7821	0.8470	0.8132	0.6853	0.9642	0.9115	0.9467
0.0384	3.21	530	0.2147	0.7923	0.8367	0.8139	0.6862	0.9648	0.9072	0.9479
0.0322	3.27	540	0.2215	0.7842	0.8489	0.8153	0.6881	0.9646	0.9126	0.9475
0.0429	3.33	550	0.2184	0.7743	0.8504	0.8106	0.6815	0.9634	0.9127	0.9463
0.0348	3.39	560	0.2293	0.7642	0.8594	0.8090	0.6792	0.9627	0.9163	0.9451
0.0365	3.45	570	0.2221	0.7922	0.8411	0.8159	0.6891	0.9651	0.9094	0.9477
0.0374	3.52	580	0.2175	0.7917	0.8382	0.8143	0.6868	0.9648	0.9079	0.9479
0.0413	3.58	590	0.2111	0.8122	0.8243	0.8182	0.6924	0.9663	0.9025	0.9499
0.0362	3.64	600	0.2183	0.7883	0.8404	0.8135	0.6856	0.9646	0.9088	0.9479
0.0352	3.7	610	0.2124	0.8005	0.8340	0.8169	0.6905	0.9656	0.9065	0.9487
0.0301	3.76	620	0.2145	0.7993	0.8369	0.8177	0.6916	0.9657	0.9078	0.9488
0.0399	3.82	630	0.2188	0.8018	0.8318	0.8166	0.6900	0.9656	0.9055	0.9485
0.0366	3.88	640	0.2211	0.7969	0.8346	0.8153	0.6882	0.9652	0.9066	0.9478
0.0289	3.94	650	0.2201	0.7850	0.8468	0.8147	0.6874	0.9646	0.9116	0.9475
0.0367	4.0	660	0.2280	0.7859	0.8437	0.8138	0.6860	0.9645	0.9102	0.9475
0.0362	4.06	670	0.2226	0.7785	0.8502	0.8128	0.6846	0.9640	0.9128	0.9469
0.0376	4.12	680	0.2213	0.8006	0.8317	0.8159	0.6890	0.9655	0.9054	0.9490
0.0294	4.18	690	0.2212	0.8052	0.8271	0.8160	0.6892	0.9657	0.9034	0.9492
0.0318	4.24	700	0.2254	0.7874	0.8420	0.8138	0.6860	0.9646	0.9095	0.9477
0.0359	4.3	710	0.2250	0.7899	0.8432	0.8157	0.6887	0.9649	0.9102	0.9479
0.034	4.36	720	0.2264	0.7985	0.8380	0.8178	0.6917	0.9656	0.9083	0.9489
0.0334	4.42	730	0.2308	0.7871	0.8436	0.8144	0.6869	0.9646	0.9102	0.9475
0.036	4.48	740	0.2250	0.7936	0.8404	0.8163	0.6896	0.9652	0.9091	0.9485
0.0257	4.55	750	0.2267	0.7861	0.8456	0.8148	0.6874	0.9646	0.9112	0.9479
0.0354	4.61	760	0.2288	0.7943	0.8401	0.8166	0.6900	0.9653	0.9090	0.9484
0.0373	4.67	770	0.2320	0.7828	0.8471	0.8137	0.6859	0.9643	0.9117	0.9470
0.0272	4.73	780	0.2250	0.7994	0.8354	0.8170	0.6906	0.9656	0.9071	0.9487
0.034	4.79	790	0.2339	0.7861	0.8450	0.8145	0.6870	0.9646	0.9108	0.9473
0.0294	4.85	800	0.2262	0.7972	0.8381	0.8171	0.6908	0.9655	0.9082	0.9486
0.0353	4.91	810	0.2337	0.7833	0.8473	0.8140	0.6864	0.9644	0.9118	0.9472
0.0337	4.97	820	0.2273	0.7973	0.8372	0.8168	0.6903	0.9655	0.9078	0.9485
0.0309	5.03	830	0.2318	0.7917	0.8413	0.8157	0.6888	0.9650	0.9094	0.9481
0.026	5.09	840	0.2327	0.7932	0.8418	0.8168	0.6903	0.9653	0.9098	0.9483
0.0271	5.15	850	0.2317	0.7887	0.8459	0.8163	0.6896	0.9650	0.9115	0.9479
0.0352	5.21	860	0.2344	0.7914	0.8427	0.8162	0.6895	0.9651	0.9101	0.9481
0.0268	5.27	870	0.2306	0.7931	0.8417	0.8166	0.6901	0.9652	0.9097	0.9484
0.0248	5.33	880	0.2309	0.7889	0.8438	0.8155	0.6884	0.9649	0.9105	0.9480
0.0331	5.39	890	0.2306	0.7895	0.8432	0.8154	0.6884	0.9649	0.9102	0.9480
0.0265	5.45	900	0.2322	0.7944	0.8401	0.8166	0.6901	0.9653	0.9091	0.9484
0.0352	5.52	910	0.2326	0.7922	0.8419	0.8163	0.6896	0.9651	0.9098	0.9482
0.0368	5.58	920	0.2313	0.7911	0.8424	0.8160	0.6891	0.9651	0.9099	0.9481
0.0315	5.64	930	0.2313	0.7917	0.8420	0.8161	0.6893	0.9651	0.9098	0.9482
0.0251	5.7	940	0.2324	0.7919	0.8409	0.8156	0.6887	0.9650	0.9093	0.9481
0.0331	5.76	950	0.2327	0.7913	0.8414	0.8156	0.6886	0.9650	0.9095	0.9481
0.0361	5.82	960	0.2327	0.7904	0.8423	0.8155	0.6885	0.9650	0.9098	0.9480
0.0362	5.88	970	0.2325	0.7909	0.8419	0.8156	0.6887	0.9650	0.9097	0.9481
0.031	5.94	980	0.2324	0.7914	0.8418	0.8158	0.6889	0.9650	0.9097	0.9481
0.0316	6.0	990	0.2324	0.7915	0.8418	0.8159	0.6890	0.9651	0.9097	0.9481
0.0232	6.06	1000	0.2324	0.7915	0.8418	0.8159	0.6890	0.9651	0.9097	0.9481

Framework versions

Transformers 4.34.1
Pytorch 2.0.1
Datasets 2.14.6
Tokenizers 0.14.1

admko/sembr2023-bert-small

sembr2023-bert-small

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions