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

加载中…

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

admko/sembr2023-bert-tiny

sembr2023-bert-tiny

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

sembr2023-bert-tiny

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions