You can deploy this model and start earning money today!

admko/
sembr2023-distilbert-base-multilingual-cased

Name: sembr2023-distilbert-base-multilingual-cased
Availability: InStock

generated_from_trainer

distilbert/distilbert-base-multilingual-cased

Views

No views yet

sembr2023-distilbert-base-multilingual-cased

This model is a fine-tuned version of distilbert-base-multilingual-cased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.2282
Precision: 0.7986
Recall: 0.8244
F1: 0.8113
Iou: 0.6825
Accuracy: 0.9666
Balanced Accuracy: 0.9023
Overall Accuracy: 0.9521

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
0.4021	0.06	10	0.3642	0	0.0	0.0	0.0	0.9130	0.5	0.9130
0.2804	0.12	20	0.2550	0.7462	0.5589	0.6391	0.4696	0.9451	0.7704	0.9331
0.2173	0.18	30	0.2038	0.9157	0.4420	0.5962	0.4247	0.9479	0.7191	0.9406
0.1831	0.24	40	0.1824	0.8317	0.7186	0.7710	0.6274	0.9629	0.8524	0.9455
0.1816	0.29	50	0.1829	0.7540	0.7857	0.7695	0.6254	0.9591	0.8807	0.9405
0.1461	0.35	60	0.1619	0.8520	0.7210	0.7810	0.6407	0.9648	0.8545	0.9498
0.1424	0.41	70	0.1568	0.8091	0.7717	0.7900	0.6528	0.9643	0.8772	0.9483
0.11	0.47	80	0.1550	0.8189	0.7760	0.7969	0.6624	0.9656	0.8798	0.9504
0.1373	0.53	90	0.1584	0.8248	0.7640	0.7933	0.6573	0.9654	0.8743	0.9484
0.1202	0.59	100	0.1612	0.8292	0.7608	0.7935	0.6577	0.9656	0.8729	0.9496
0.0966	0.65	110	0.1390	0.8575	0.7597	0.8057	0.6746	0.9681	0.8738	0.9551
0.0832	0.71	120	0.1569	0.8292	0.7858	0.8069	0.6763	0.9673	0.8852	0.9508
0.0914	0.76	130	0.1399	0.8295	0.8009	0.8150	0.6877	0.9684	0.8926	0.9529
0.0793	0.82	140	0.1456	0.8161	0.7914	0.8035	0.6716	0.9663	0.8872	0.9512
0.0903	0.88	150	0.1461	0.8218	0.7882	0.8047	0.6732	0.9667	0.8860	0.9523
0.0976	0.94	160	0.1380	0.7999	0.8304	0.8148	0.6875	0.9672	0.9053	0.9533
0.0776	1.0	170	0.1633	0.8053	0.8144	0.8099	0.6805	0.9667	0.8978	0.9492
0.0722	1.06	180	0.1581	0.8569	0.7739	0.8133	0.6853	0.9691	0.8808	0.9528
0.0787	1.12	190	0.1563	0.7780	0.8350	0.8055	0.6743	0.9649	0.9062	0.9495
0.0642	1.18	200	0.1483	0.8007	0.8115	0.8060	0.6751	0.9660	0.8961	0.9527
0.0582	1.24	210	0.1559	0.8244	0.8067	0.8154	0.6884	0.9682	0.8952	0.9534
0.0617	1.29	220	0.1359	0.8158	0.8115	0.8136	0.6858	0.9677	0.8970	0.9560
0.0508	1.35	230	0.1638	0.8372	0.7872	0.8114	0.6827	0.9682	0.8863	0.9548
0.049	1.41	240	0.1699	0.8585	0.7432	0.7967	0.6620	0.9670	0.8657	0.9523
0.0604	1.47	250	0.1452	0.7935	0.8374	0.8148	0.6875	0.9669	0.9083	0.9523
0.0512	1.53	260	0.1551	0.8353	0.7820	0.8078	0.6776	0.9676	0.8837	0.9542
0.0496	1.59	270	0.1686	0.7757	0.8490	0.8107	0.6816	0.9655	0.9128	0.9496
0.0486	1.65	280	0.1474	0.8464	0.7834	0.8137	0.6859	0.9688	0.8849	0.9557
0.0529	1.71	290	0.1470	0.8114	0.8136	0.8125	0.6842	0.9673	0.8978	0.9535
0.0585	1.76	300	0.1625	0.8193	0.8057	0.8124	0.6841	0.9676	0.8944	0.9527
0.0373	1.82	310	0.1414	0.8038	0.8240	0.8138	0.6860	0.9672	0.9024	0.9555
0.0474	1.88	320	0.1600	0.8117	0.8073	0.8095	0.6800	0.9670	0.8947	0.9541
0.0475	1.94	330	0.1825	0.7709	0.8418	0.8048	0.6733	0.9645	0.9090	0.9498
0.0596	2.0	340	0.1688	0.8185	0.8078	0.8131	0.6851	0.9677	0.8954	0.9533
0.0391	2.06	350	0.1775	0.8006	0.8142	0.8073	0.6769	0.9662	0.8974	0.9514
0.0285	2.12	360	0.1653	0.8047	0.8165	0.8105	0.6814	0.9668	0.8988	0.9536
0.0288	2.18	370	0.1855	0.7697	0.8376	0.8022	0.6698	0.9641	0.9069	0.9509
0.0244	2.24	380	0.1813	0.8053	0.8171	0.8112	0.6823	0.9669	0.8991	0.9530
0.0324	2.29	390	0.1663	0.8029	0.8252	0.8139	0.6862	0.9672	0.9030	0.9538
0.0306	2.35	400	0.1692	0.7949	0.8291	0.8117	0.6830	0.9665	0.9044	0.9529
0.0279	2.41	410	0.1812	0.8171	0.8056	0.8113	0.6825	0.9674	0.8942	0.9526
0.0287	2.47	420	0.1768	0.8196	0.8106	0.8151	0.6879	0.9680	0.8968	0.9540
0.0287	2.53	430	0.1849	0.7927	0.8271	0.8095	0.6800	0.9662	0.9032	0.9532
0.0328	2.59	440	0.1765	0.8031	0.8197	0.8113	0.6825	0.9668	0.9003	0.9536
0.0226	2.65	450	0.1928	0.7879	0.8403	0.8133	0.6853	0.9664	0.9094	0.9515
0.0308	2.71	460	0.1905	0.7858	0.8354	0.8098	0.6804	0.9659	0.9069	0.9515
0.0277	2.76	470	0.1890	0.8083	0.8168	0.8125	0.6842	0.9672	0.8992	0.9537
0.0229	2.82	480	0.1802	0.8125	0.8177	0.8151	0.6879	0.9677	0.8999	0.9540
0.0218	2.88	490	0.1895	0.7805	0.8370	0.8078	0.6775	0.9654	0.9073	0.9506
0.0283	2.94	500	0.1926	0.7854	0.8337	0.8088	0.6790	0.9657	0.9060	0.9516
0.0231	3.0	510	0.2023	0.7766	0.8432	0.8085	0.6786	0.9653	0.9101	0.9509
0.0172	3.06	520	0.2051	0.7811	0.8305	0.8051	0.6737	0.9650	0.9042	0.9509
0.0206	3.12	530	0.1918	0.7894	0.8339	0.8110	0.6822	0.9662	0.9064	0.9523
0.0243	3.18	540	0.1982	0.7992	0.8198	0.8094	0.6798	0.9664	0.9001	0.9526
0.0193	3.24	550	0.2036	0.8024	0.8165	0.8094	0.6798	0.9666	0.8987	0.9517
0.0212	3.29	560	0.1967	0.8093	0.8175	0.8134	0.6854	0.9674	0.8996	0.9535
0.0188	3.35	570	0.1944	0.8056	0.8188	0.8122	0.6837	0.9671	0.9000	0.9527
0.0176	3.41	580	0.1975	0.8014	0.8239	0.8125	0.6842	0.9669	0.9022	0.9528
0.0197	3.47	590	0.2118	0.8058	0.8186	0.8122	0.6837	0.9671	0.8999	0.9529
0.0142	3.53	600	0.2000	0.8107	0.8187	0.8147	0.6873	0.9676	0.9003	0.9540
0.0145	3.59	610	0.2095	0.7950	0.8278	0.8111	0.6822	0.9665	0.9037	0.9522
0.023	3.65	620	0.2107	0.7881	0.8268	0.8070	0.6764	0.9656	0.9028	0.9511
0.0156	3.71	630	0.2191	0.7814	0.8366	0.8081	0.6780	0.9654	0.9072	0.9507
0.0175	3.76	640	0.2090	0.8081	0.8202	0.8141	0.6865	0.9674	0.9008	0.9528
0.02	3.82	650	0.2160	0.8068	0.8224	0.8145	0.6871	0.9674	0.9018	0.9524
0.0148	3.88	660	0.2049	0.7972	0.8306	0.8136	0.6857	0.9669	0.9052	0.9526
0.0184	3.94	670	0.2122	0.7908	0.8342	0.8119	0.6834	0.9664	0.9066	0.9513
0.0178	4.0	680	0.2090	0.7925	0.8271	0.8094	0.6799	0.9661	0.9032	0.9519
0.0159	4.06	690	0.2137	0.7963	0.8293	0.8125	0.6842	0.9667	0.9045	0.9528
0.0172	4.12	700	0.2150	0.7917	0.8317	0.8112	0.6824	0.9663	0.9054	0.9520
0.0163	4.18	710	0.2174	0.8010	0.8250	0.8128	0.6846	0.9670	0.9027	0.9524
0.0117	4.24	720	0.2175	0.8036	0.8229	0.8131	0.6851	0.9671	0.9019	0.9530
0.0149	4.29	730	0.2225	0.7990	0.8242	0.8114	0.6826	0.9667	0.9022	0.9515
0.014	4.35	740	0.2157	0.7916	0.8273	0.8090	0.6793	0.9660	0.9033	0.9520
0.0139	4.41	750	0.2217	0.7952	0.8231	0.8089	0.6792	0.9662	0.9015	0.9512
0.0143	4.47	760	0.2201	0.7914	0.8317	0.8111	0.6822	0.9663	0.9054	0.9518
0.0147	4.53	770	0.2218	0.7945	0.8264	0.8101	0.6809	0.9663	0.9030	0.9517
0.0124	4.59	780	0.2300	0.7915	0.8337	0.8120	0.6835	0.9664	0.9064	0.9515
0.0157	4.65	790	0.2244	0.8038	0.8191	0.8114	0.6826	0.9669	0.9000	0.9520
0.0126	4.71	800	0.2324	0.7983	0.8256	0.8117	0.6831	0.9667	0.9029	0.9514
0.0137	4.76	810	0.2263	0.8000	0.8226	0.8112	0.6823	0.9667	0.9015	0.9520
0.0133	4.82	820	0.2295	0.7932	0.8299	0.8111	0.6823	0.9664	0.9046	0.9512
0.0155	4.88	830	0.2266	0.8019	0.8226	0.8121	0.6837	0.9669	0.9016	0.9520
0.0135	4.94	840	0.2286	0.7932	0.8318	0.8121	0.6836	0.9665	0.9056	0.9518
0.0073	5.0	850	0.2283	0.8019	0.8235	0.8125	0.6843	0.9670	0.9021	0.9520
0.0147	5.06	860	0.2292	0.7965	0.8290	0.8124	0.6841	0.9667	0.9044	0.9518
0.0139	5.12	870	0.2293	0.8013	0.8251	0.8130	0.6850	0.9670	0.9028	0.9520
0.0162	5.18	880	0.2277	0.7977	0.8261	0.8116	0.6830	0.9667	0.9031	0.9518
0.0111	5.24	890	0.2285	0.7941	0.8288	0.8111	0.6822	0.9664	0.9042	0.9517
0.0128	5.29	900	0.2273	0.7987	0.8240	0.8112	0.6823	0.9666	0.9021	0.9521
0.0129	5.35	910	0.2282	0.7989	0.8244	0.8114	0.6827	0.9667	0.9023	0.9520
0.0115	5.41	920	0.2285	0.7991	0.8246	0.8117	0.6830	0.9667	0.9024	0.9520
0.011	5.47	930	0.2283	0.7999	0.8242	0.8119	0.6833	0.9668	0.9023	0.9522
0.013	5.53	940	0.2281	0.7995	0.8239	0.8115	0.6828	0.9667	0.9021	0.9522
0.0132	5.59	950	0.2281	0.7993	0.8239	0.8114	0.6826	0.9667	0.9021	0.9522
0.0104	5.65	960	0.2281	0.7992	0.8244	0.8116	0.6829	0.9667	0.9023	0.9522
0.0143	5.71	970	0.2279	0.7988	0.8245	0.8114	0.6827	0.9667	0.9024	0.9521
0.0133	5.76	980	0.2280	0.7988	0.8241	0.8113	0.6825	0.9667	0.9022	0.9521
0.0079	5.82	990	0.2282	0.7986	0.8244	0.8113	0.6825	0.9666	0.9023	0.9521
0.0108	5.88	1000	0.2282	0.7986	0.8244	0.8113	0.6825	0.9666	0.9023	0.9521

Framework versions

Transformers 4.34.1
Pytorch 2.0.1
Datasets 2.14.6
Tokenizers 0.14.1

admko/sembr2023-distilbert-base-multilingual-cased

sembr2023-distilbert-base-multilingual-cased

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

admko/
sembr2023-distilbert-base-multilingual-cased