sembr2023-bert-tiny

admko/sembr2023-bert-tiny

sembr2023-bert-tiny 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: Nov 28, 2023
SHA: 601af2ce

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Base models

prajjwal1/bert-tiny

Model card

From the Hugging Face model README

sembr2023-bert-tiny

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

Loss: 0.2101
Precision: 0.7983
Recall: 0.6561
F1: 0.7202
Iou: 0.5628
Accuracy: 0.9531
Balanced Accuracy: 0.8196
Overall Accuracy: 0.9387

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
1.2554	0.06	10	1.1550	0	0.0	0.0	0.0	0.9080	0.5	0.9080
0.8047	0.12	20	0.7616	0	0.0	0.0	0.0	0.9080	0.5	0.9080
0.6392	0.18	30	0.6116	0	0.0	0.0	0.0	0.9080	0.5	0.9080
0.5328	0.24	40	0.5384	0	0.0	0.0	0.0	0.9080	0.5	0.9080
0.4859	0.3	50	0.4982	0	0.0	0.0	0.0	0.9080	0.5	0.9080
0.469	0.36	60	0.4726	0	0.0	0.0	0.0	0.9080	0.5	0.9080
0.4711	0.42	70	0.4513	0	0.0	0.0	0.0	0.9080	0.5	0.9080
0.4341	0.48	80	0.4349	0	0.0	0.0	0.0	0.9080	0.5	0.9080
0.4234	0.55	90	0.4181	0	0.0	0.0	0.0	0.9080	0.5	0.9080
0.3661	0.61	100	0.3970	0	0.0	0.0	0.0	0.9080	0.5	0.9080
0.3901	0.67	110	0.3685	0	0.0	0.0	0.0	0.9080	0.5	0.9080
0.3493	0.73	120	0.3447	0.6074	0.0126	0.0247	0.0125	0.9084	0.5059	0.9081
0.3199	0.79	130	0.3309	0.6329	0.0676	0.1222	0.0651	0.9106	0.5318	0.9095
0.3444	0.85	140	0.3219	0.6748	0.1406	0.2328	0.1317	0.9147	0.5669	0.9130
0.3131	0.91	150	0.3158	0.6768	0.2211	0.3334	0.2000	0.9187	0.6052	0.9154
0.2921	0.97	160	0.3100	0.7245	0.1708	0.2765	0.1604	0.9178	0.5821	0.9156
0.3121	1.03	170	0.3057	0.6425	0.3246	0.4313	0.2749	0.9213	0.6531	0.9157
0.3267	1.09	180	0.3035	0.6597	0.3155	0.4269	0.2714	0.9221	0.6495	0.9168
0.28	1.15	190	0.2986	0.6836	0.3429	0.4567	0.2960	0.9250	0.6634	0.9171
0.2945	1.21	200	0.2929	0.7005	0.3078	0.4276	0.2720	0.9242	0.6472	0.9177
0.2744	1.27	210	0.2874	0.7108	0.3406	0.4606	0.2992	0.9266	0.6633	0.9183
0.2563	1.33	220	0.2866	0.6712	0.4432	0.5339	0.3641	0.9288	0.7106	0.9182
0.2565	1.39	230	0.2793	0.7057	0.4187	0.5256	0.3565	0.9305	0.7005	0.9203
0.2383	1.45	240	0.2760	0.6918	0.4493	0.5448	0.3744	0.9309	0.7145	0.9197
0.2477	1.52	250	0.2698	0.7317	0.4190	0.5328	0.3632	0.9324	0.7017	0.9218
0.2466	1.58	260	0.2674	0.7119	0.4605	0.5593	0.3882	0.9332	0.7208	0.9212
0.2623	1.64	270	0.2641	0.7071	0.4675	0.5629	0.3917	0.9332	0.7240	0.9220
0.2308	1.7	280	0.2622	0.7169	0.4797	0.5748	0.4033	0.9347	0.7303	0.9225
0.2179	1.76	290	0.2577	0.7287	0.4678	0.5698	0.3984	0.9350	0.7251	0.9236
0.2347	1.82	300	0.2557	0.7425	0.4651	0.5719	0.4005	0.9360	0.7244	0.9246
0.2175	1.88	310	0.2549	0.7314	0.4873	0.5849	0.4133	0.9364	0.7346	0.9244
0.2365	1.94	320	0.2524	0.7237	0.5057	0.5954	0.4239	0.9368	0.7431	0.9244
0.2068	2.0	330	0.2513	0.7569	0.4744	0.5832	0.4117	0.9376	0.7295	0.9260
0.2004	2.06	340	0.2506	0.6962	0.5462	0.6122	0.4411	0.9363	0.7611	0.9234
0.231	2.12	350	0.2490	0.7145	0.5251	0.6053	0.4340	0.9370	0.7519	0.9241
0.2117	2.18	360	0.2457	0.7300	0.5132	0.6027	0.4314	0.9378	0.7470	0.9257
0.1768	2.24	370	0.2450	0.7281	0.5273	0.6116	0.4405	0.9384	0.7537	0.9256
0.2013	2.3	380	0.2433	0.7198	0.5513	0.6244	0.4539	0.9390	0.7648	0.9258
0.2128	2.36	390	0.2405	0.7568	0.5214	0.6174	0.4466	0.9406	0.7522	0.9282
0.2186	2.42	400	0.2393	0.7560	0.5215	0.6173	0.4464	0.9405	0.7522	0.9279
0.2105	2.48	410	0.2408	0.6966	0.5834	0.6350	0.4652	0.9383	0.7788	0.9246
0.2216	2.55	420	0.2382	0.7415	0.5493	0.6311	0.4610	0.9409	0.7650	0.9277
0.1816	2.61	430	0.2377	0.7258	0.5768	0.6428	0.4736	0.9410	0.7774	0.9274
0.2136	2.67	440	0.2352	0.7506	0.5456	0.6319	0.4619	0.9415	0.7636	0.9284
0.2043	2.73	450	0.2341	0.7425	0.5615	0.6394	0.4700	0.9418	0.7709	0.9286
0.2014	2.79	460	0.2333	0.7565	0.5572	0.6417	0.4725	0.9428	0.7695	0.9297
0.1862	2.85	470	0.2306	0.7744	0.5520	0.6446	0.4755	0.9440	0.7678	0.9313
0.1714	2.91	480	0.2312	0.7354	0.6083	0.6658	0.4991	0.9438	0.7931	0.9302
0.1693	2.97	490	0.2280	0.7637	0.5768	0.6572	0.4895	0.9447	0.7794	0.9314
0.2043	3.03	500	0.2288	0.7577	0.5848	0.6601	0.4927	0.9446	0.7830	0.9314
0.2138	3.09	510	0.2256	0.7797	0.5650	0.6552	0.4872	0.9453	0.7744	0.9327
0.1914	3.15	520	0.2250	0.7732	0.5873	0.6675	0.5010	0.9462	0.7849	0.9330
0.1647	3.21	530	0.2240	0.7586	0.6173	0.6807	0.5160	0.9467	0.7987	0.9329
0.1749	3.27	540	0.2237	0.7679	0.6108	0.6804	0.5156	0.9472	0.7961	0.9331
0.1883	3.33	550	0.2226	0.7839	0.5992	0.6792	0.5143	0.9479	0.7913	0.9344
0.1657	3.39	560	0.2196	0.7856	0.6059	0.6841	0.5199	0.9485	0.7946	0.9353
0.1721	3.45	570	0.2217	0.7556	0.6408	0.6935	0.5308	0.9479	0.8099	0.9335
0.1843	3.52	580	0.2188	0.7935	0.6010	0.6840	0.5197	0.9489	0.7926	0.9354
0.1709	3.58	590	0.2175	0.7993	0.6078	0.6905	0.5273	0.9499	0.7962	0.9364
0.1526	3.64	600	0.2168	0.7782	0.6380	0.7012	0.5398	0.9500	0.8098	0.9358
0.1614	3.7	610	0.2148	0.8129	0.6083	0.6959	0.5336	0.9511	0.7971	0.9380
0.1585	3.76	620	0.2149	0.8046	0.6210	0.7010	0.5396	0.9513	0.8029	0.9377
0.1798	3.82	630	0.2163	0.7788	0.6476	0.7072	0.5470	0.9507	0.8145	0.9364
0.1637	3.88	640	0.2147	0.8000	0.6276	0.7034	0.5425	0.9513	0.8059	0.9375
0.1542	3.94	650	0.2138	0.8004	0.6335	0.7072	0.5471	0.9518	0.8088	0.9379
0.1575	4.0	660	0.2146	0.7867	0.6464	0.7097	0.5500	0.9514	0.8143	0.9371
0.1632	4.06	670	0.2124	0.7998	0.6368	0.7091	0.5493	0.9519	0.8103	0.9380
0.1687	4.12	680	0.2112	0.8129	0.6294	0.7095	0.5498	0.9526	0.8074	0.9390
0.1565	4.18	690	0.2129	0.7959	0.6429	0.7113	0.5519	0.9520	0.8131	0.9380
0.1869	4.24	700	0.2128	0.7896	0.6526	0.7146	0.5559	0.9521	0.8175	0.9378
0.1689	4.3	710	0.2119	0.8052	0.6361	0.7107	0.5512	0.9524	0.8102	0.9385
0.1581	4.36	720	0.2126	0.7817	0.6618	0.7167	0.5585	0.9519	0.8215	0.9373
0.1683	4.42	730	0.2121	0.8019	0.6442	0.7145	0.5558	0.9526	0.8140	0.9384
0.1735	4.48	740	0.2111	0.8009	0.6452	0.7147	0.5560	0.9526	0.8145	0.9387
0.1537	4.55	750	0.2104	0.7991	0.6461	0.7145	0.5558	0.9525	0.8148	0.9386
0.174	4.61	760	0.2112	0.8031	0.6454	0.7156	0.5572	0.9528	0.8147	0.9387
0.1662	4.67	770	0.2118	0.7897	0.6586	0.7182	0.5603	0.9525	0.8204	0.9378
0.1486	4.73	780	0.2113	0.8009	0.6492	0.7171	0.5590	0.9529	0.8164	0.9386
0.1672	4.79	790	0.2110	0.8055	0.6461	0.7170	0.5589	0.9531	0.8152	0.9389
0.1553	4.85	800	0.2108	0.7969	0.6527	0.7176	0.5596	0.9528	0.8179	0.9383
0.1504	4.91	810	0.2106	0.8047	0.6461	0.7167	0.5585	0.9530	0.8151	0.9389
0.176	4.97	820	0.2103	0.8059	0.6459	0.7171	0.5589	0.9531	0.8151	0.9389
0.1597	5.03	830	0.2102	0.7979	0.6535	0.7185	0.5607	0.9529	0.8184	0.9386
0.1437	5.09	840	0.2105	0.7977	0.6539	0.7187	0.5609	0.9529	0.8185	0.9385
0.1751	5.15	850	0.2104	0.8004	0.6508	0.7179	0.5600	0.9530	0.8172	0.9386
0.1737	5.21	860	0.2105	0.7951	0.6573	0.7197	0.5621	0.9529	0.8201	0.9385
0.1683	5.27	870	0.2104	0.7953	0.6573	0.7198	0.5622	0.9529	0.8201	0.9385
0.1477	5.33	880	0.2102	0.7974	0.6536	0.7184	0.5605	0.9529	0.8184	0.9386
0.1702	5.39	890	0.2102	0.7978	0.6532	0.7183	0.5604	0.9529	0.8182	0.9386
0.1478	5.45	900	0.2101	0.7985	0.6536	0.7188	0.5611	0.9530	0.8185	0.9386
0.1656	5.52	910	0.2099	0.8	0.6522	0.7186	0.5608	0.9530	0.8179	0.9387
0.1757	5.58	920	0.2099	0.7996	0.6525	0.7186	0.5608	0.9530	0.8180	0.9387
0.1723	5.64	930	0.2100	0.7990	0.6536	0.7190	0.5613	0.9530	0.8185	0.9387
0.1472	5.7	940	0.2101	0.7976	0.6561	0.7199	0.5624	0.9531	0.8196	0.9386
0.1628	5.76	950	0.2102	0.7974	0.6564	0.7201	0.5626	0.9531	0.8198	0.9386
0.1563	5.82	960	0.2102	0.7973	0.6564	0.7200	0.5626	0.9531	0.8198	0.9386
0.1893	5.88	970	0.2102	0.7979	0.6563	0.7202	0.5628	0.9531	0.8197	0.9387
0.1554	5.94	980	0.2101	0.7982	0.6562	0.7203	0.5628	0.9531	0.8197	0.9387
0.1636	6.0	990	0.2101	0.7983	0.6561	0.7202	0.5628	0.9531	0.8196	0.9387
0.1588	6.06	1000	0.2101	0.7983	0.6561	0.7202	0.5628	0.9531	0.8196	0.9387

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: Nov 28, 2023