sembr2023-distilbert-base-cased

admko/sembr2023-distilbert-base-cased

sembr2023-distilbert-base-cased 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 apache-2.0.

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

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At a glance

Task: Token Classification
Library: transformers
License: apache-2.0
Model type: distilbert
Access: Public
Created: Nov 27, 2023
Updated: Nov 28, 2023
SHA: fcf94d4f

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Base models

Model card

From the Hugging Face model README

sembr2023-distilbert-base-cased

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

Loss: 0.2214
Precision: 0.7952
Recall: 0.8261
F1: 0.8104
Iou: 0.6812
Accuracy: 0.9665
Balanced Accuracy: 0.9030
Overall Accuracy: 0.9525

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
0.3717	0.06	10	0.3886	0	0.0	0.0	0.0	0.9133	0.5	0.9133
0.3692	0.12	20	0.3552	0	0.0	0.0	0.0	0.9133	0.5	0.9133
0.2579	0.17	30	0.2734	0.8923	0.1841	0.3052	0.1801	0.9274	0.5910	0.9255
0.2135	0.23	40	0.2224	0.7632	0.6267	0.6882	0.5247	0.9508	0.8041	0.9348
0.225	0.29	50	0.1935	0.8426	0.6378	0.7260	0.5699	0.9583	0.8132	0.9427
0.1637	0.35	60	0.1785	0.8115	0.6951	0.7488	0.5985	0.9596	0.8399	0.9470
0.1497	0.4	70	0.1837	0.8159	0.6961	0.7513	0.6016	0.9601	0.8406	0.9434
0.1248	0.46	80	0.1758	0.7920	0.7523	0.7717	0.6282	0.9614	0.8668	0.9446
0.1297	0.52	90	0.1796	0.7740	0.7933	0.7835	0.6441	0.9620	0.8857	0.9430
0.1321	0.58	100	0.1721	0.8616	0.7178	0.7832	0.6436	0.9656	0.8534	0.9496
0.1058	0.64	110	0.1572	0.8132	0.7766	0.7945	0.6591	0.9652	0.8799	0.9494
0.1183	0.69	120	0.1734	0.8084	0.7792	0.7935	0.6578	0.9649	0.8809	0.9470
0.1195	0.75	130	0.1652	0.7753	0.7952	0.7851	0.6462	0.9623	0.8867	0.9463
0.0996	0.81	140	0.1433	0.8292	0.7684	0.7977	0.6634	0.9662	0.8767	0.9527
0.1009	0.87	150	0.1817	0.8181	0.7808	0.7990	0.6653	0.9660	0.8822	0.9480
0.0953	0.92	160	0.1554	0.8669	0.7245	0.7893	0.6519	0.9665	0.8570	0.9524
0.1077	0.98	170	0.1556	0.8261	0.7752	0.7998	0.6664	0.9664	0.8798	0.9512
0.0981	1.04	180	0.1526	0.8283	0.7703	0.7982	0.6642	0.9663	0.8776	0.9520
0.0982	1.1	190	0.1547	0.8001	0.7976	0.7989	0.6651	0.9652	0.8894	0.9504
0.0789	1.16	200	0.1606	0.8135	0.7947	0.8040	0.6722	0.9664	0.8887	0.9513
0.0829	1.21	210	0.1566	0.8244	0.7872	0.8054	0.6741	0.9670	0.8856	0.9517
0.0742	1.27	220	0.1680	0.8167	0.7895	0.8029	0.6707	0.9664	0.8864	0.9506
0.084	1.33	230	0.1680	0.8197	0.7824	0.8006	0.6675	0.9662	0.8830	0.9511
0.0702	1.39	240	0.1653	0.8184	0.7996	0.8089	0.6791	0.9673	0.8914	0.9510
0.0713	1.45	250	0.1675	0.7844	0.8184	0.8010	0.6681	0.9648	0.8985	0.9492
0.0763	1.5	260	0.1501	0.8239	0.7833	0.8031	0.6710	0.9667	0.8837	0.9532
0.0738	1.56	270	0.1518	0.8203	0.7962	0.8081	0.6780	0.9672	0.8898	0.9527
0.0736	1.62	280	0.1624	0.7849	0.8222	0.8031	0.6710	0.9651	0.9004	0.9508
0.0659	1.68	290	0.1735	0.7775	0.8308	0.8033	0.6712	0.9647	0.9041	0.9496
0.0653	1.73	300	0.1586	0.7828	0.8224	0.8022	0.6697	0.9648	0.9004	0.9503
0.0635	1.79	310	0.1720	0.8091	0.8033	0.8062	0.6753	0.9665	0.8927	0.9510
0.0724	1.85	320	0.1588	0.8057	0.8033	0.8045	0.6729	0.9662	0.8925	0.9531
0.0612	1.91	330	0.1818	0.7828	0.8222	0.8020	0.6695	0.9648	0.9003	0.9488
0.0612	1.97	340	0.1704	0.8235	0.7893	0.8060	0.6751	0.9671	0.8866	0.9526
0.0592	2.02	350	0.1634	0.8002	0.7929	0.7965	0.6618	0.9649	0.8870	0.9520
0.0474	2.08	360	0.1835	0.7931	0.8120	0.8025	0.6701	0.9654	0.8960	0.9506
0.0484	2.14	370	0.1790	0.8123	0.7941	0.8031	0.6710	0.9663	0.8883	0.9522
0.0524	2.2	380	0.1812	0.7702	0.8291	0.7985	0.6646	0.9637	0.9028	0.9499
0.052	2.25	390	0.1716	0.8041	0.7964	0.8002	0.6670	0.9655	0.8890	0.9533
0.0443	2.31	400	0.1676	0.8054	0.7976	0.8015	0.6687	0.9658	0.8897	0.9535
0.057	2.37	410	0.1836	0.8028	0.8084	0.8056	0.6745	0.9662	0.8948	0.9507
0.0414	2.43	420	0.1791	0.8049	0.8053	0.8051	0.6737	0.9662	0.8934	0.9527
0.0471	2.49	430	0.1771	0.7964	0.8126	0.8044	0.6728	0.9658	0.8965	0.9527
0.039	2.54	440	0.1773	0.8066	0.8021	0.8043	0.6727	0.9662	0.8919	0.9537
0.0543	2.6	450	0.1855	0.7887	0.8193	0.8037	0.6718	0.9653	0.8992	0.9511
0.0398	2.66	460	0.1959	0.7938	0.8147	0.8041	0.6724	0.9656	0.8973	0.9504
0.0419	2.72	470	0.1944	0.7847	0.8286	0.8060	0.6751	0.9654	0.9035	0.9498
0.0436	2.77	480	0.1869	0.8002	0.8109	0.8055	0.6744	0.9661	0.8958	0.9520
0.0497	2.83	490	0.1850	0.7736	0.8422	0.8065	0.6757	0.9650	0.9094	0.9501
0.0408	2.89	500	0.1883	0.8178	0.7962	0.8068	0.6762	0.9670	0.8897	0.9527
0.0332	2.95	510	0.1883	0.7913	0.8188	0.8048	0.6733	0.9656	0.8991	0.9516
0.0382	3.01	520	0.2008	0.7914	0.8307	0.8106	0.6815	0.9664	0.9049	0.9515
0.047	3.06	530	0.1913	0.8137	0.8013	0.8075	0.6771	0.9669	0.8920	0.9522
0.0327	3.12	540	0.1969	0.7993	0.8168	0.8080	0.6778	0.9664	0.8987	0.9518
0.0338	3.18	550	0.1989	0.7962	0.8173	0.8066	0.6759	0.9660	0.8987	0.9518
0.0332	3.24	560	0.2004	0.7999	0.8178	0.8087	0.6789	0.9665	0.8992	0.9518
0.0308	3.29	570	0.1964	0.8126	0.8092	0.8109	0.6819	0.9673	0.8957	0.9537
0.0348	3.35	580	0.2032	0.7902	0.8239	0.8067	0.6761	0.9658	0.9016	0.9515
0.0351	3.41	590	0.2064	0.7855	0.8218	0.8032	0.6712	0.9651	0.9003	0.9511
0.0301	3.47	600	0.2118	0.7872	0.8265	0.8063	0.6755	0.9656	0.9026	0.9505
0.0261	3.53	610	0.1997	0.7991	0.8194	0.8091	0.6794	0.9665	0.8999	0.9522
0.0282	3.58	620	0.1950	0.8029	0.8114	0.8071	0.6766	0.9664	0.8962	0.9527
0.0326	3.64	630	0.2038	0.7873	0.8290	0.8076	0.6773	0.9658	0.9039	0.9516
0.0353	3.7	640	0.2010	0.7930	0.8228	0.8076	0.6773	0.9660	0.9012	0.9514
0.0348	3.76	650	0.2043	0.7949	0.8243	0.8093	0.6797	0.9663	0.9021	0.9519
0.0296	3.82	660	0.2050	0.7976	0.8226	0.8099	0.6805	0.9665	0.9014	0.9529
0.0287	3.87	670	0.2158	0.7820	0.8318	0.8061	0.6752	0.9653	0.9049	0.9504
0.024	3.93	680	0.2110	0.7847	0.8294	0.8065	0.6757	0.9655	0.9039	0.9512
0.0274	3.99	690	0.2075	0.7937	0.8254	0.8092	0.6796	0.9663	0.9025	0.9523
0.0247	4.05	700	0.2130	0.7995	0.8210	0.8101	0.6808	0.9666	0.9007	0.9525
0.0202	4.1	710	0.2142	0.7955	0.8215	0.8083	0.6782	0.9662	0.9007	0.9518
0.0245	4.16	720	0.2120	0.7965	0.8195	0.8078	0.6776	0.9662	0.8998	0.9516
0.0214	4.22	730	0.2151	0.7899	0.8256	0.8074	0.6770	0.9659	0.9024	0.9515
0.0202	4.28	740	0.2145	0.7963	0.8220	0.8089	0.6792	0.9664	0.9010	0.9520
0.0257	4.34	750	0.2181	0.7960	0.8217	0.8087	0.6788	0.9663	0.9009	0.9520
0.0271	4.39	760	0.2151	0.7953	0.8232	0.8090	0.6793	0.9663	0.9015	0.9518
0.0279	4.45	770	0.2196	0.7955	0.8237	0.8094	0.6798	0.9664	0.9018	0.9521
0.0273	4.51	780	0.2194	0.7984	0.8256	0.8118	0.6832	0.9668	0.9029	0.9523
0.018	4.57	790	0.2201	0.7985	0.8247	0.8114	0.6826	0.9668	0.9025	0.9526
0.0275	4.62	800	0.2204	0.7893	0.8358	0.8119	0.6834	0.9664	0.9073	0.9519
0.0198	4.68	810	0.2160	0.7983	0.8232	0.8105	0.6814	0.9666	0.9017	0.9526
0.019	4.74	820	0.2109	0.7961	0.8243	0.8100	0.6806	0.9665	0.9021	0.9527
0.0236	4.8	830	0.2208	0.7956	0.8238	0.8094	0.6799	0.9664	0.9019	0.9521
0.0177	4.86	840	0.2217	0.7900	0.8301	0.8095	0.6800	0.9661	0.9046	0.9519
0.0209	4.91	850	0.2226	0.7927	0.8285	0.8102	0.6810	0.9664	0.9040	0.9522
0.0241	4.97	860	0.2215	0.7915	0.8276	0.8091	0.6794	0.9662	0.9035	0.9521
0.0211	5.03	870	0.2181	0.7957	0.8242	0.8097	0.6802	0.9664	0.9020	0.9525
0.0234	5.09	880	0.2171	0.7975	0.8224	0.8098	0.6803	0.9665	0.9013	0.9526
0.0201	5.14	890	0.2191	0.7925	0.8265	0.8092	0.6795	0.9662	0.9030	0.9523
0.0211	5.2	900	0.2175	0.7957	0.8238	0.8095	0.6799	0.9664	0.9019	0.9526
0.0234	5.26	910	0.2207	0.7913	0.8291	0.8097	0.6803	0.9662	0.9042	0.9522
0.023	5.32	920	0.2202	0.7965	0.8234	0.8098	0.6803	0.9665	0.9017	0.9524
0.0192	5.38	930	0.2203	0.7969	0.8239	0.8102	0.6809	0.9665	0.9020	0.9525
0.0217	5.43	940	0.2206	0.7956	0.8255	0.8103	0.6811	0.9665	0.9027	0.9524
0.0195	5.49	950	0.2213	0.7953	0.8259	0.8103	0.6811	0.9665	0.9029	0.9524
0.0285	5.55	960	0.2214	0.7955	0.8254	0.8102	0.6809	0.9665	0.9026	0.9524
0.0263	5.61	970	0.2213	0.7955	0.8254	0.8102	0.6809	0.9665	0.9026	0.9524
0.02	5.66	980	0.2214	0.7951	0.8258	0.8101	0.6809	0.9665	0.9028	0.9524
0.021	5.72	990	0.2214	0.7952	0.8261	0.8104	0.6812	0.9665	0.9030	0.9525
0.0233	5.78	1000	0.2214	0.7952	0.8261	0.8104	0.6812	0.9665	0.9030	0.9525

Framework versions

Transformers 4.34.1
Pytorch 2.0.1
Datasets 2.14.6
Tokenizers 0.14.1

Task: Token Classification
Library: transformers
Type: distilbert
License: apache-2.0
Created: Nov 27, 2023
Updated: Nov 28, 2023