segformer-b1-finetuned-segments-sidewalks-6

This model is a fine-tuned version of nvidia/mit-b1 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0281
• Mean Iou: 0.7734
• Mean Accuracy: 0.8242
• Overall Accuracy: 0.9921
• Accuracy Bkg: 0.9973
• Accuracy Knife: 0.6511
• Accuracy Gun: nan
• Iou Bkg: 0.9920
• Iou Knife: 0.5548
• Iou Gun: nan

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: 6e-05
• train_batch_size: 6
• eval_batch_size: 6
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Bkg	Accuracy Knife	Accuracy Gun	Iou Bkg	Iou Knife	Iou Gun
0.755	0.1613	20	0.6655	0.3281	0.4997	0.9841	0.9993	0.0002	nan	0.9841	0.0002	0.0
0.5966	0.3226	40	0.4548	0.3440	0.5334	0.9799	0.9939	0.0728	nan	0.9799	0.0521	0.0
0.5055	0.4839	60	0.4195	0.3744	0.6108	0.9791	0.9906	0.2311	nan	0.9790	0.1441	0.0
0.3988	0.6452	80	0.3316	0.5972	0.6199	0.9861	0.9975	0.2422	nan	0.9860	0.2085	nan
0.3471	0.8065	100	0.2676	0.6187	0.6461	0.9867	0.9973	0.2949	nan	0.9866	0.2509	nan
0.2608	0.9677	120	0.2266	0.6353	0.6712	0.9868	0.9966	0.3457	nan	0.9867	0.2838	nan
0.2139	1.1290	140	0.1955	0.6424	0.6891	0.9864	0.9957	0.3825	nan	0.9863	0.2985	nan
0.1642	1.2903	160	0.1454	0.6391	0.6643	0.9878	0.9979	0.3307	nan	0.9877	0.2904	nan
0.1391	1.4516	180	0.1125	0.6450	0.6657	0.9883	0.9984	0.3330	nan	0.9883	0.3017	nan
0.1115	1.6129	200	0.0950	0.6473	0.6632	0.9888	0.9989	0.3274	nan	0.9887	0.3059	nan
0.1074	1.7742	220	0.0867	0.6669	0.6906	0.9890	0.9983	0.3828	nan	0.9889	0.3449	nan
0.0838	1.9355	240	0.0774	0.6813	0.7068	0.9894	0.9983	0.4154	nan	0.9894	0.3732	nan
0.0762	2.0968	260	0.0717	0.6959	0.7398	0.9892	0.9969	0.4827	nan	0.9891	0.4027	nan
0.0708	2.2581	280	0.0756	0.7036	0.7833	0.9880	0.9944	0.5722	nan	0.9879	0.4193	nan
0.0596	2.4194	300	0.0599	0.7063	0.7545	0.9894	0.9968	0.5123	nan	0.9894	0.4233	nan
0.0655	2.5806	320	0.0621	0.7189	0.7816	0.9894	0.9959	0.5673	nan	0.9893	0.4485	nan
0.0578	2.7419	340	0.0568	0.7269	0.7940	0.9897	0.9958	0.5923	nan	0.9896	0.4643	nan
0.0481	2.9032	360	0.0506	0.7208	0.7571	0.9905	0.9978	0.5165	nan	0.9904	0.4511	nan
0.0479	3.0645	380	0.0490	0.7181	0.7532	0.9904	0.9978	0.5087	nan	0.9903	0.4458	nan
0.0471	3.2258	400	0.0473	0.7237	0.7575	0.9907	0.9980	0.5170	nan	0.9906	0.4568	nan
0.044	3.3871	420	0.0451	0.7329	0.7725	0.9908	0.9977	0.5474	nan	0.9908	0.4751	nan
0.039	3.5484	440	0.0465	0.7370	0.7848	0.9907	0.9972	0.5725	nan	0.9907	0.4834	nan
0.0372	3.7097	460	0.0429	0.7366	0.7860	0.9907	0.9971	0.5749	nan	0.9906	0.4826	nan
0.0389	3.8710	480	0.0399	0.7297	0.7668	0.9908	0.9978	0.5358	nan	0.9907	0.4687	nan
0.0347	4.0323	500	0.0390	0.7306	0.7607	0.9911	0.9983	0.5232	nan	0.9910	0.4701	nan
0.0331	4.1935	520	0.0408	0.7430	0.7949	0.9909	0.9970	0.5927	nan	0.9908	0.4953	nan
0.0417	4.3548	540	0.0367	0.7454	0.7885	0.9912	0.9975	0.5795	nan	0.9911	0.4996	nan
0.0379	4.5161	560	0.0381	0.7515	0.8102	0.9910	0.9966	0.6237	nan	0.9909	0.5120	nan
0.029	4.6774	580	0.0352	0.7479	0.7862	0.9915	0.9979	0.5745	nan	0.9914	0.5045	nan
0.0405	4.8387	600	0.0375	0.7547	0.8267	0.9908	0.9959	0.6574	nan	0.9907	0.5187	nan
0.0284	5.0	620	0.0365	0.7498	0.8161	0.9907	0.9962	0.6361	nan	0.9906	0.5091	nan
0.0294	5.1613	640	0.0362	0.7533	0.8184	0.9909	0.9963	0.6406	nan	0.9908	0.5157	nan
0.0296	5.3226	660	0.0347	0.7486	0.8032	0.9910	0.9969	0.6095	nan	0.9909	0.5062	nan
0.0228	5.4839	680	0.0340	0.7434	0.7820	0.9913	0.9978	0.5663	nan	0.9912	0.4955	nan
0.0251	5.6452	700	0.0339	0.7509	0.7983	0.9913	0.9973	0.5992	nan	0.9912	0.5106	nan
0.0255	5.8065	720	0.0319	0.7388	0.7663	0.9915	0.9985	0.5342	nan	0.9914	0.4862	nan
0.0248	5.9677	740	0.0324	0.7450	0.7835	0.9913	0.9978	0.5692	nan	0.9913	0.4988	nan
0.0249	6.1290	760	0.0322	0.7556	0.8044	0.9914	0.9973	0.6116	nan	0.9914	0.5199	nan
0.0291	6.2903	780	0.0339	0.7524	0.8363	0.9903	0.9951	0.6775	nan	0.9902	0.5146	nan
0.0191	6.4516	800	0.0334	0.7549	0.8350	0.9905	0.9954	0.6747	nan	0.9904	0.5194	nan
0.0228	6.6129	820	0.0311	0.7556	0.8047	0.9914	0.9973	0.6121	nan	0.9914	0.5199	nan
0.0233	6.7742	840	0.0304	0.7588	0.8101	0.9915	0.9972	0.6229	nan	0.9914	0.5262	nan
0.0215	6.9355	860	0.0308	0.7604	0.8271	0.9912	0.9963	0.6579	nan	0.9911	0.5297	nan
0.0197	7.0968	880	0.0305	0.7626	0.8258	0.9913	0.9965	0.6551	nan	0.9913	0.5338	nan
0.0206	7.2581	900	0.0294	0.7534	0.7929	0.9916	0.9978	0.5879	nan	0.9915	0.5152	nan
0.0311	7.4194	920	0.0298	0.7618	0.8185	0.9915	0.9969	0.6402	nan	0.9914	0.5322	nan
0.0175	7.5806	940	0.0299	0.7534	0.7971	0.9915	0.9976	0.5967	nan	0.9914	0.5154	nan
0.0228	7.7419	960	0.0294	0.7545	0.7956	0.9916	0.9977	0.5934	nan	0.9915	0.5175	nan
0.0215	7.9032	980	0.0294	0.7612	0.8147	0.9915	0.9971	0.6323	nan	0.9915	0.5310	nan
0.0177	8.0645	1000	0.0291	0.7608	0.8152	0.9915	0.9970	0.6334	nan	0.9914	0.5302	nan
0.0201	8.2258	1020	0.0295	0.7594	0.8148	0.9914	0.9969	0.6326	nan	0.9913	0.5274	nan
0.0182	8.3871	1040	0.0289	0.7617	0.8175	0.9915	0.9969	0.6380	nan	0.9914	0.5319	nan
0.019	8.5484	1060	0.0287	0.7612	0.8154	0.9915	0.9970	0.6337	nan	0.9914	0.5310	nan
0.0165	8.7097	1080	0.0280	0.7624	0.8129	0.9917	0.9972	0.6286	nan	0.9916	0.5331	nan
0.02	8.8710	1100	0.0289	0.7617	0.8253	0.9913	0.9965	0.6540	nan	0.9912	0.5322	nan
0.0211	9.0323	1120	0.0283	0.7582	0.8006	0.9917	0.9977	0.6035	nan	0.9916	0.5248	nan
0.0139	9.1935	1140	0.0282	0.7632	0.8152	0.9917	0.9972	0.6333	nan	0.9916	0.5348	nan
0.0225	9.3548	1160	0.0281	0.7614	0.8072	0.9918	0.9975	0.6168	nan	0.9917	0.5310	nan
0.02	9.5161	1180	0.0287	0.7605	0.8199	0.9914	0.9967	0.6432	nan	0.9913	0.5297	nan
0.0184	9.6774	1200	0.0289	0.7557	0.8004	0.9916	0.9975	0.6033	nan	0.9915	0.5199	nan
0.0192	9.8387	1220	0.0286	0.7593	0.8088	0.9916	0.9973	0.6203	nan	0.9915	0.5270	nan
0.0188	10.0	1240	0.0286	0.7645	0.8252	0.9915	0.9967	0.6537	nan	0.9914	0.5375	nan
0.0167	10.1613	1260	0.0284	0.7670	0.8390	0.9913	0.9961	0.6820	nan	0.9912	0.5427	nan
0.0162	10.3226	1280	0.0273	0.7667	0.8271	0.9916	0.9967	0.6574	nan	0.9915	0.5420	nan
0.0154	10.4839	1300	0.0276	0.7610	0.8048	0.9918	0.9976	0.6119	nan	0.9917	0.5303	nan
0.015	10.6452	1320	0.0279	0.7612	0.8102	0.9917	0.9973	0.6231	nan	0.9916	0.5308	nan
0.0151	10.8065	1340	0.0281	0.7630	0.8207	0.9915	0.9968	0.6445	nan	0.9914	0.5346	nan
0.0157	10.9677	1360	0.0278	0.7648	0.8234	0.9916	0.9968	0.6501	nan	0.9915	0.5381	nan
0.0145	11.1290	1380	0.0273	0.7642	0.8145	0.9917	0.9973	0.6317	nan	0.9917	0.5367	nan
0.0136	11.2903	1400	0.0275	0.7670	0.8284	0.9916	0.9967	0.6602	nan	0.9915	0.5425	nan
0.0125	11.4516	1420	0.0274	0.7680	0.8253	0.9917	0.9969	0.6537	nan	0.9916	0.5444	nan
0.0147	11.6129	1440	0.0279	0.7668	0.8289	0.9915	0.9966	0.6613	nan	0.9915	0.5421	nan
0.0188	11.7742	1460	0.0274	0.7667	0.8295	0.9915	0.9966	0.6624	nan	0.9914	0.5420	nan
0.0141	11.9355	1480	0.0272	0.7664	0.8232	0.9917	0.9969	0.6495	nan	0.9916	0.5413	nan
0.0148	12.0968	1500	0.0276	0.7636	0.8092	0.9918	0.9975	0.6208	nan	0.9918	0.5354	nan
0.012	12.2581	1520	0.0268	0.7697	0.8302	0.9917	0.9967	0.6636	nan	0.9916	0.5477	nan
0.0106	12.4194	1540	0.0269	0.7715	0.8350	0.9917	0.9966	0.6735	nan	0.9916	0.5514	nan
0.0137	12.5806	1560	0.0269	0.7705	0.8228	0.9919	0.9972	0.6484	nan	0.9919	0.5492	nan
0.018	12.7419	1580	0.0270	0.7721	0.8315	0.9918	0.9968	0.6661	nan	0.9917	0.5525	nan
0.012	12.9032	1600	0.0269	0.7672	0.8245	0.9917	0.9969	0.6521	nan	0.9916	0.5429	nan
0.0151	13.0645	1620	0.0270	0.7670	0.8174	0.9919	0.9973	0.6374	nan	0.9918	0.5423	nan
0.0133	13.2258	1640	0.0270	0.7695	0.8183	0.9920	0.9974	0.6393	nan	0.9919	0.5470	nan
0.0146	13.3871	1660	0.0278	0.7687	0.8440	0.9913	0.9959	0.6921	nan	0.9912	0.5462	nan
0.0114	13.5484	1680	0.0269	0.7705	0.8326	0.9917	0.9967	0.6686	nan	0.9916	0.5495	nan
0.0159	13.7097	1700	0.0272	0.7690	0.8306	0.9916	0.9967	0.6646	nan	0.9916	0.5464	nan
0.0193	13.8710	1720	0.0272	0.7677	0.8167	0.9919	0.9974	0.6360	nan	0.9918	0.5435	nan
0.0112	14.0323	1740	0.0271	0.7695	0.8215	0.9919	0.9972	0.6457	nan	0.9918	0.5471	nan
0.0117	14.1935	1760	0.0278	0.7618	0.8048	0.9918	0.9977	0.6120	nan	0.9918	0.5318	nan
0.0169	14.3548	1780	0.0276	0.7686	0.8455	0.9913	0.9958	0.6952	nan	0.9912	0.5461	nan
0.0117	14.5161	1800	0.0274	0.7711	0.8412	0.9915	0.9962	0.6862	nan	0.9914	0.5508	nan
0.0139	14.6774	1820	0.0266	0.7718	0.8304	0.9918	0.9969	0.6639	nan	0.9918	0.5518	nan
0.0138	14.8387	1840	0.0273	0.7703	0.8285	0.9918	0.9969	0.6602	nan	0.9917	0.5489	nan
0.0142	15.0	1860	0.0271	0.7679	0.8128	0.9920	0.9976	0.6279	nan	0.9919	0.5439	nan
0.0113	15.1613	1880	0.0273	0.7707	0.8353	0.9916	0.9965	0.6741	nan	0.9916	0.5499	nan
0.0093	15.3226	1900	0.0269	0.7664	0.8087	0.9920	0.9978	0.6196	nan	0.9920	0.5407	nan
0.0119	15.4839	1920	0.0279	0.7623	0.7984	0.9920	0.9981	0.5986	nan	0.9920	0.5327	nan
0.0149	15.6452	1940	0.0270	0.7679	0.8138	0.9920	0.9976	0.6301	nan	0.9919	0.5439	nan
0.0128	15.8065	1960	0.0269	0.7700	0.8248	0.9919	0.9971	0.6524	nan	0.9918	0.5483	nan
0.0145	15.9677	1980	0.0273	0.7702	0.8212	0.9920	0.9973	0.6450	nan	0.9919	0.5485	nan
0.0101	16.1290	2000	0.0268	0.7713	0.8250	0.9919	0.9971	0.6530	nan	0.9919	0.5508	nan
0.0128	16.2903	2020	0.0276	0.7685	0.8195	0.9919	0.9973	0.6417	nan	0.9918	0.5451	nan
0.0097	16.4516	2040	0.0274	0.7675	0.8247	0.9917	0.9969	0.6525	nan	0.9916	0.5434	nan
0.0129	16.6129	2060	0.0274	0.7685	0.8196	0.9919	0.9973	0.6420	nan	0.9918	0.5452	nan
0.0122	16.7742	2080	0.0269	0.7707	0.8249	0.9919	0.9971	0.6528	nan	0.9918	0.5495	nan
0.0143	16.9355	2100	0.0273	0.7695	0.8264	0.9918	0.9970	0.6558	nan	0.9917	0.5473	nan
0.0091	17.0968	2120	0.0277	0.7699	0.8273	0.9918	0.9969	0.6577	nan	0.9917	0.5482	nan
0.0096	17.2581	2140	0.0268	0.7712	0.8267	0.9919	0.9970	0.6563	nan	0.9918	0.5505	nan
0.0094	17.4194	2160	0.0270	0.7707	0.8235	0.9919	0.9972	0.6499	nan	0.9919	0.5496	nan
0.0108	17.5806	2180	0.0270	0.7718	0.8240	0.9920	0.9972	0.6508	nan	0.9919	0.5517	nan
0.0123	17.7419	2200	0.0283	0.7646	0.8030	0.9921	0.9980	0.6079	nan	0.9920	0.5372	nan
0.0097	17.9032	2220	0.0268	0.7731	0.8300	0.9919	0.9970	0.6631	nan	0.9918	0.5544	nan
0.0103	18.0645	2240	0.0272	0.7725	0.8291	0.9919	0.9970	0.6612	nan	0.9918	0.5532	nan
0.01	18.2258	2260	0.0268	0.7717	0.8275	0.9919	0.9970	0.6579	nan	0.9918	0.5516	nan
0.0132	18.3871	2280	0.0270	0.7725	0.8441	0.9915	0.9962	0.6921	nan	0.9915	0.5536	nan
0.0086	18.5484	2300	0.0269	0.7716	0.8270	0.9919	0.9971	0.6569	nan	0.9918	0.5513	nan
0.011	18.7097	2320	0.0269	0.7737	0.8299	0.9920	0.9970	0.6628	nan	0.9919	0.5554	nan
0.0152	18.8710	2340	0.0270	0.7721	0.8241	0.9920	0.9973	0.6509	nan	0.9919	0.5523	nan
0.0103	19.0323	2360	0.0273	0.7719	0.8231	0.9920	0.9973	0.6489	nan	0.9919	0.5518	nan
0.0087	19.1935	2380	0.0272	0.7686	0.8116	0.9921	0.9977	0.6255	nan	0.9920	0.5452	nan
0.0108	19.3548	2400	0.0270	0.7726	0.8236	0.9921	0.9973	0.6500	nan	0.9920	0.5532	nan
0.0064	19.5161	2420	0.0274	0.7725	0.8220	0.9921	0.9974	0.6465	nan	0.9920	0.5529	nan
0.0135	19.6774	2440	0.0278	0.7658	0.8053	0.9921	0.9979	0.6127	nan	0.9920	0.5396	nan
0.0109	19.8387	2460	0.0274	0.7691	0.8116	0.9921	0.9978	0.6255	nan	0.9921	0.5461	nan
0.0078	20.0	2480	0.0273	0.7707	0.8197	0.9920	0.9974	0.6420	nan	0.9920	0.5494	nan
0.0087	20.1613	2500	0.0276	0.7677	0.8083	0.9921	0.9979	0.6187	nan	0.9921	0.5433	nan
0.0101	20.3226	2520	0.0276	0.7688	0.8111	0.9921	0.9978	0.6245	nan	0.9920	0.5456	nan
0.0126	20.4839	2540	0.0271	0.7721	0.8190	0.9921	0.9975	0.6404	nan	0.9921	0.5522	nan
0.0102	20.6452	2560	0.0276	0.7688	0.8133	0.9921	0.9976	0.6290	nan	0.9920	0.5455	nan
0.0112	20.8065	2580	0.0273	0.7732	0.8376	0.9917	0.9966	0.6786	nan	0.9917	0.5546	nan
0.0099	20.9677	2600	0.0272	0.7729	0.8360	0.9918	0.9966	0.6754	nan	0.9917	0.5541	nan
0.0103	21.1290	2620	0.0279	0.7686	0.8144	0.9920	0.9976	0.6313	nan	0.9919	0.5452	nan
0.0089	21.2903	2640	0.0273	0.7719	0.8269	0.9919	0.9971	0.6567	nan	0.9918	0.5520	nan
0.0094	21.4516	2660	0.0278	0.7687	0.8169	0.9920	0.9974	0.6364	nan	0.9919	0.5456	nan
0.0077	21.6129	2680	0.0276	0.7697	0.8199	0.9920	0.9973	0.6424	nan	0.9919	0.5476	nan
0.0106	21.7742	2700	0.0277	0.7688	0.8125	0.9921	0.9977	0.6272	nan	0.9920	0.5456	nan
0.0128	21.9355	2720	0.0277	0.7727	0.8277	0.9920	0.9971	0.6583	nan	0.9919	0.5535	nan
0.0098	22.0968	2740	0.0275	0.7723	0.8258	0.9920	0.9972	0.6544	nan	0.9919	0.5528	nan
0.0102	22.2581	2760	0.0279	0.7717	0.8249	0.9920	0.9972	0.6526	nan	0.9919	0.5515	nan
0.0093	22.4194	2780	0.0275	0.7733	0.8310	0.9919	0.9969	0.6650	nan	0.9918	0.5548	nan
0.0092	22.5806	2800	0.0277	0.7715	0.8219	0.9920	0.9973	0.6465	nan	0.9919	0.5510	nan
0.0131	22.7419	2820	0.0279	0.7704	0.8227	0.9919	0.9972	0.6482	nan	0.9919	0.5490	nan
0.0136	22.9032	2840	0.0276	0.7719	0.8236	0.9920	0.9973	0.6500	nan	0.9919	0.5519	nan
0.0108	23.0645	2860	0.0278	0.7709	0.8220	0.9920	0.9973	0.6467	nan	0.9919	0.5499	nan
0.0098	23.2258	2880	0.0289	0.7641	0.8033	0.9920	0.9979	0.6087	nan	0.9920	0.5363	nan
0.0103	23.3871	2900	0.0280	0.7697	0.8174	0.9920	0.9975	0.6373	nan	0.9919	0.5474	nan
0.0074	23.5484	2920	0.0280	0.7709	0.8204	0.9920	0.9974	0.6434	nan	0.9919	0.5498	nan
0.0133	23.7097	2940	0.0276	0.7729	0.8285	0.9920	0.9971	0.6599	nan	0.9919	0.5539	nan
0.0092	23.8710	2960	0.0274	0.7734	0.8317	0.9919	0.9969	0.6665	nan	0.9918	0.5551	nan
0.0088	24.0323	2980	0.0279	0.7723	0.8212	0.9921	0.9974	0.6450	nan	0.9920	0.5525	nan
0.0092	24.1935	3000	0.0282	0.7698	0.8136	0.9921	0.9977	0.6295	nan	0.9920	0.5475	nan
0.0088	24.3548	3020	0.0278	0.7716	0.8186	0.9921	0.9975	0.6397	nan	0.9920	0.5511	nan
0.0086	24.5161	3040	0.0279	0.7727	0.8252	0.9920	0.9972	0.6532	nan	0.9919	0.5535	nan
0.0091	24.6774	3060	0.0282	0.7707	0.8189	0.9920	0.9975	0.6404	nan	0.9920	0.5494	nan
0.0078	24.8387	3080	0.0278	0.7718	0.8223	0.9920	0.9973	0.6473	nan	0.9920	0.5516	nan
0.0081	25.0	3100	0.0281	0.7708	0.8206	0.9920	0.9974	0.6439	nan	0.9919	0.5497	nan
0.0075	25.1613	3120	0.0283	0.7707	0.8199	0.9920	0.9974	0.6423	nan	0.9919	0.5494	nan
0.0087	25.3226	3140	0.0276	0.7734	0.8296	0.9920	0.9970	0.6621	nan	0.9919	0.5548	nan
0.011	25.4839	3160	0.0284	0.7691	0.8131	0.9921	0.9977	0.6284	nan	0.9920	0.5462	nan
0.0106	25.6452	3180	0.0278	0.7729	0.8219	0.9921	0.9974	0.6463	nan	0.9920	0.5537	nan
0.0093	25.8065	3200	0.0277	0.7740	0.8285	0.9920	0.9971	0.6599	nan	0.9919	0.5561	nan
0.0118	25.9677	3220	0.0274	0.7748	0.8317	0.9920	0.9970	0.6664	nan	0.9919	0.5577	nan
0.0176	26.1290	3240	0.0280	0.7702	0.8149	0.9921	0.9977	0.6322	nan	0.9920	0.5484	nan
0.0101	26.2903	3260	0.0282	0.7718	0.8222	0.9920	0.9973	0.6471	nan	0.9920	0.5517	nan
0.0124	26.4516	3280	0.0281	0.7725	0.8232	0.9921	0.9973	0.6491	nan	0.9920	0.5531	nan
0.0069	26.6129	3300	0.0282	0.7716	0.8206	0.9921	0.9974	0.6438	nan	0.9920	0.5513	nan
0.0088	26.7742	3320	0.0282	0.7716	0.8207	0.9921	0.9974	0.6440	nan	0.9920	0.5512	nan
0.0119	26.9355	3340	0.0279	0.7729	0.8238	0.9921	0.9973	0.6503	nan	0.9920	0.5538	nan
0.0071	27.0968	3360	0.0278	0.7737	0.8274	0.9920	0.9972	0.6577	nan	0.9920	0.5555	nan
0.009	27.2581	3380	0.0282	0.7719	0.8201	0.9921	0.9975	0.6428	nan	0.9920	0.5519	nan
0.0089	27.4194	3400	0.0283	0.7720	0.8185	0.9921	0.9976	0.6394	nan	0.9921	0.5519	nan
0.008	27.5806	3420	0.0278	0.7749	0.8277	0.9921	0.9972	0.6581	nan	0.9920	0.5578	nan
0.0083	27.7419	3440	0.0279	0.7734	0.8216	0.9922	0.9975	0.6458	nan	0.9921	0.5547	nan
0.0085	27.9032	3460	0.0281	0.7725	0.8205	0.9921	0.9975	0.6436	nan	0.9920	0.5530	nan
0.0093	28.0645	3480	0.0282	0.7727	0.8217	0.9921	0.9974	0.6461	nan	0.9920	0.5533	nan
0.0096	28.2258	3500	0.0281	0.7741	0.8260	0.9921	0.9973	0.6547	nan	0.9920	0.5562	nan
0.0085	28.3871	3520	0.0280	0.7737	0.8238	0.9921	0.9974	0.6503	nan	0.9920	0.5554	nan
0.0099	28.5484	3540	0.0279	0.7729	0.8206	0.9921	0.9975	0.6437	nan	0.9921	0.5537	nan
0.0107	28.7097	3560	0.0280	0.7738	0.8247	0.9921	0.9973	0.6521	nan	0.9920	0.5556	nan
0.0093	28.8710	3580	0.0279	0.7733	0.8227	0.9921	0.9974	0.6480	nan	0.9920	0.5545	nan
0.0092	29.0323	3600	0.0281	0.7720	0.8192	0.9921	0.9975	0.6409	nan	0.9920	0.5520	nan
0.0086	29.1935	3620	0.0279	0.7732	0.8231	0.9921	0.9974	0.6488	nan	0.9920	0.5545	nan
0.0083	29.3548	3640	0.0280	0.7731	0.8220	0.9921	0.9974	0.6467	nan	0.9920	0.5541	nan
0.0083	29.5161	3660	0.0281	0.7726	0.8207	0.9921	0.9975	0.6439	nan	0.9920	0.5531	nan
0.0079	29.6774	3680	0.0281	0.7728	0.8218	0.9921	0.9974	0.6461	nan	0.9920	0.5536	nan
0.0083	29.8387	3700	0.0281	0.7731	0.8229	0.9921	0.9974	0.6484	nan	0.9920	0.5542	nan
0.0072	30.0	3720	0.0281	0.7734	0.8242	0.9921	0.9973	0.6511	nan	0.9920	0.5548	nan

Framework versions

• Transformers 4.44.2
• Pytorch 2.4.1+cu121
• Datasets 3.0.1
• Tokenizers 0.19.1