Training Loss	Epoch	Step	Validation Loss
No log	0.02	8000	2.2672
2.4371	0.03	16000	2.1833
2.4371	0.05	24000	2.1433
2.2336	0.07	32000	2.1114
2.2336	0.08	40000	2.0954
2.181	0.1	48000	2.0750
2.181	0.11	56000	2.0664
2.1553	0.13	64000	2.0569
2.1553	0.15	72000	2.0540
2.1321	0.16	80000	2.0405
2.1321	0.18	88000	2.0337
2.1249	0.2	96000	2.0336
2.1249	0.21	104000	2.0284
2.1127	0.23	112000	2.0238
2.1127	0.25	120000	2.0200
2.1049	0.26	128000	2.0201
2.1049	0.28	136000	2.0145
2.099	0.3	144000	2.0094
2.099	0.31	152000	2.0080
2.0896	0.33	160000	2.0121
2.0896	0.34	168000	2.0086
2.0859	0.36	176000	2.0072
2.0859	0.38	184000	2.0053
2.086	0.39	192000	2.0015
2.086	0.41	200000	2.0021
2.0742	0.43	208000	1.9942
2.0742	0.44	216000	1.9983
2.0777	0.46	224000	2.0000
2.0777	0.48	232000	1.9983
2.0781	0.49	240000	1.9971
2.0781	0.51	248000	1.9951
2.0695	0.52	256000	1.9953
2.0695	0.54	264000	1.9983
2.0767	0.56	272000	1.9923
2.0767	0.57	280000	1.9947
2.0694	0.59	288000	1.9892
2.0694	0.61	296000	1.9908
2.0679	0.62	304000	1.9927
2.0679	0.64	312000	1.9948
2.0754	0.66	320000	1.9922
2.0754	0.67	328000	1.9916
2.0572	0.69	336000	1.9912
2.0572	0.7	344000	1.9875
2.0652	0.72	352000	1.9935
2.0652	0.74	360000	1.9859
2.0662	0.75	368000	1.9912
2.0662	0.77	376000	1.9916
2.06	0.79	384000	1.9943
2.06	0.8	392000	1.9894
2.0688	0.82	400000	1.9861
2.0688	0.84	408000	1.9876
2.0668	0.85	416000	1.9848
2.0668	0.87	424000	1.9855
2.0686	0.89	432000	1.9920
2.0686	0.9	440000	1.9825
2.0677	0.92	448000	1.9886
2.0677	0.93	456000	1.9914
2.0649	0.95	464000	1.9864
2.0649	0.97	472000	1.9866
2.0665	0.98	480000	1.9869
2.0665	1.0	488000	1.9853
2.0586	1.02	496000	1.9900
2.0586	1.03	504000	1.9859
2.0609	1.05	512000	1.9851
2.0609	1.07	520000	1.9795
2.0647	1.08	528000	1.9859
2.0647	1.1	536000	1.9813
2.0568	1.11	544000	1.9843
2.0568	1.13	552000	1.9871
2.0538	1.15	560000	1.9815
2.0538	1.16	568000	1.9774
2.0569	1.18	576000	1.9896
2.0569	1.2	584000	1.9813
2.0619	1.21	592000	1.9865
2.0619	1.23	600000	1.9780
2.06	1.25	608000	1.9861
2.06	1.26	616000	1.9843
2.057	1.28	624000	1.9829
2.057	1.29	632000	1.9843
2.0581	1.31	640000	1.9765
2.0581	1.33	648000	1.9796
2.0466	1.34	656000	1.9807
2.0466	1.36	664000	1.9814
2.0561	1.38	672000	1.9844
2.0561	1.39	680000	1.9829
2.0536	1.41	688000	1.9855
2.0536	1.43	696000	1.9843
2.0468	1.44	704000	1.9824
2.0468	1.46	712000	1.9776
2.0454	1.48	720000	1.9799
2.0454	1.49	728000	1.9845
2.0529	1.51	736000	1.9683
2.0529	1.52	744000	1.9847
2.0529	1.54	752000	1.9838
2.0529	1.56	760000	1.9772
2.0551	1.57	768000	1.9751
2.0551	1.59	776000	1.9825
2.0535	1.61	784000	1.9835
2.0535	1.62	792000	1.9798
2.0494	1.64	800000	1.9811
2.0494	1.66	808000	1.9803
2.0479	1.67	816000	1.9783
2.0479	1.69	824000	1.9752
2.0485	1.7	832000	1.9735
2.0485	1.72	840000	1.9773
2.0481	1.74	848000	1.9755
2.0481	1.75	856000	1.9810
2.0523	1.77	864000	1.9770
2.0523	1.79	872000	1.9788
2.0436	1.8	880000	1.9785
2.0436	1.82	888000	1.9740
2.0527	1.84	896000	1.9770
2.0527	1.85	904000	1.9757
2.0504	1.87	912000	1.9818
2.0504	1.88	920000	1.9851
2.0552	1.9	928000	1.9748
2.0552	1.92	936000	1.9807
2.0512	1.93	944000	1.9803
2.0512	1.95	952000	1.9812
2.0472	1.97	960000	1.9808
2.0472	1.98	968000	1.9725
2.0485	2.0	976000	1.9746
2.0485	2.02	984000	1.9762
2.048	2.03	992000	1.9753
2.048	2.05	1000000	1.9741
2.0485	2.07	1008000	1.9785
2.0485	2.08	1016000	1.9792
2.0421	2.1	1024000	1.9731
2.0421	2.11	1032000	1.9797
2.0497	2.13	1040000	1.9812
2.0497	2.15	1048000	1.9764
2.0457	2.16	1056000	1.9763
2.0457	2.18	1064000	1.9769
2.0539	2.2	1072000	1.9792
2.0539	2.21	1080000	1.9767
2.0462	2.23	1088000	1.9796
2.0462	2.25	1096000	1.9789
2.0568	2.26	1104000	1.9752
2.0568	2.28	1112000	1.9795
2.0443	2.29	1120000	1.9762
2.0443	2.31	1128000	1.9770
2.0478	2.33	1136000	1.9769
2.0478	2.34	1144000	1.9785
2.0439	2.36	1152000	1.9757
2.0439	2.38	1160000	1.9733
2.0488	2.39	1168000	1.9755
2.0488	2.41	1176000	1.9775
2.0474	2.43	1184000	1.9751
2.0474	2.44	1192000	1.9793
2.0465	2.46	1200000	1.9799
2.0465	2.47	1208000	1.9811
2.0525	2.49	1216000	1.9787
2.0525	2.51	1224000	1.9740
2.0419	2.52	1232000	1.9754
2.0419	2.54	1240000	1.9754
2.042	2.56	1248000	1.9757
2.042	2.57	1256000	1.9800
2.0499	2.59	1264000	1.9793
2.0499	2.61	1272000	1.9768
2.0448	2.62	1280000	1.9735
2.0448	2.64	1288000	1.9736
2.041	2.66	1296000	1.9741
2.041	2.67	1304000	1.9729
2.0402	2.69	1312000	1.9761
2.0402	2.7	1320000	1.9737
2.0491	2.72	1328000	1.9771
2.0491	2.74	1336000	1.9766
2.0422	2.75	1344000	1.9685
2.0422	2.77	1352000	1.9726
2.0436	2.79	1360000	1.9756
2.0436	2.8	1368000	1.9712
2.0482	2.82	1376000	1.9736
2.0482	2.84	1384000	1.9778
2.0443	2.85	1392000	1.9731
2.0443	2.87	1400000	1.9734
2.0409	2.88	1408000	1.9756
2.0409	2.9	1416000	1.9742
2.0418	2.92	1424000	1.9765
2.0418	2.93	1432000	1.9715
2.0512	2.95	1440000	1.9771
2.0512	2.97	1448000	1.9796
2.0437	2.98	1456000	1.9751
2.0437	3.0	1464000	1.9719
2.0412	3.02	1472000	1.9798
2.0412	3.03	1480000	1.9811
2.0368	3.05	1488000	1.9770
2.0368	3.07	1496000	1.9765
2.0401	3.08	1504000	1.9666
2.0401	3.1	1512000	1.9774
2.048	3.11	1520000	1.9710
2.048	3.13	1528000	1.9769
2.0478	3.15	1536000	1.9749
2.0478	3.16	1544000	1.9754
2.0441	3.18	1552000	1.9767
2.0441	3.2	1560000	1.9732
2.0458	3.21	1568000	1.9834
2.0458	3.23	1576000	1.9801
2.043	3.25	1584000	1.9764
2.043	3.26	1592000	1.9745
2.0451	3.28	1600000	1.9755
2.0451	3.29	1608000	1.9747
2.0446	3.31	1616000	1.9775
2.0446	3.33	1624000	1.9732
2.041	3.34	1632000	1.9716
2.041	3.36	1640000	1.9712
2.046	3.38	1648000	1.9726
2.046	3.39	1656000	1.9765
2.0367	3.41	1664000	1.9826
2.0367	3.43	1672000	1.9764
2.0454	3.44	1680000	1.9751
2.0454	3.46	1688000	1.9797
2.0415	3.47	1696000	1.9762
2.0415	3.49	1704000	1.9738
2.0447	3.51	1712000	1.9806
2.0447	3.52	1720000	1.9783
2.0457	3.54	1728000	1.9783
2.0457	3.56	1736000	1.9816
2.0471	3.57	1744000	1.9734
2.0471	3.59	1752000	1.9732
2.0463	3.61	1760000	1.9754
2.0463	3.62	1768000	1.9782
2.0435	3.64	1776000	1.9815
2.0435	3.66	1784000	1.9803
2.0415	3.67	1792000	1.9751
2.0415	3.69	1800000	1.9780
2.0541	3.7	1808000	1.9681
2.0541	3.72	1816000	1.9716
2.0533	3.74	1824000	1.9790
2.0533	3.75	1832000	1.9783
2.0451	3.77	1840000	1.9786
2.0451	3.79	1848000	1.9780
2.044	3.8	1856000	1.9781
2.044	3.82	1864000	1.9692
2.0525	3.84	1872000	1.9778
2.0525	3.85	1880000	1.9724
2.0521	3.87	1888000	1.9753
2.0521	3.88	1896000	1.9798
2.0421	3.9	1904000	1.9810
2.0421	3.92	1912000	1.9785
2.0402	3.93	1920000	1.9822
2.0402	3.95	1928000	1.9793
2.0393	3.97	1936000	1.9740
2.0393	3.98	1944000	1.9773
2.043	4.0	1952000	1.9709
2.043	4.02	1960000	1.9765
2.0437	4.03	1968000	1.9737
2.0437	4.05	1976000	1.9728
2.038	4.06	1984000	1.9737
2.038	4.08	1992000	1.9798
2.0486	4.1	2000000	1.9739
2.0486	4.11	2008000	1.9744
2.0344	4.13	2016000	1.9778
2.0344	4.15	2024000	1.9747
2.0487	4.16	2032000	1.9737
2.0487	4.18	2040000	1.9762
2.0443	4.2	2048000	1.9779
2.0443	4.21	2056000	1.9791
2.0401	4.23	2064000	1.9796
2.0401	4.25	2072000	1.9716
2.045	4.26	2080000	1.9752
2.045	4.28	2088000	1.9775
2.0416	4.29	2096000	1.9792
2.0416	4.31	2104000	1.9763
2.0474	4.33	2112000	1.9738
2.0474	4.34	2120000	1.9784
2.0413	4.36	2128000	1.9759
2.0413	4.38	2136000	1.9748
2.0424	4.39	2144000	1.9747
2.0424	4.41	2152000	1.9807
2.0415	4.43	2160000	1.9758
2.0415	4.44	2168000	1.9744
2.0385	4.46	2176000	1.9745
2.0385	4.47	2184000	1.9718
2.0488	4.49	2192000	1.9787
2.0488	4.51	2200000	1.9765
2.0464	4.52	2208000	1.9776
2.0464	4.54	2216000	1.9745
2.0433	4.56	2224000	1.9753
2.0433	4.57	2232000	1.9749
2.0483	4.59	2240000	1.9775
2.0483	4.61	2248000	1.9770
2.0448	4.62	2256000	1.9763
2.0448	4.64	2264000	1.9797
2.0459	4.65	2272000	1.9792
2.0459	4.67	2280000	1.9749
2.0453	4.69	2288000	1.9779
2.0453	4.7	2296000	1.9759
2.0413	4.72	2304000	1.9797
2.0413	4.74	2312000	1.9702
2.0439	4.75	2320000	1.9795
2.0439	4.77	2328000	1.9745
2.0447	4.79	2336000	1.9748
2.0447	4.8	2344000	1.9709
2.0416	4.82	2352000	1.9775
2.0416	4.84	2360000	1.9811
2.0445	4.85	2368000	1.9830
2.0445	4.87	2376000	1.9771
2.05	4.88	2384000	1.9766
2.05	4.9	2392000	1.9731
2.0439	4.92	2400000	1.9746

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu117
Datasets 2.13.1
Tokenizers 0.13.3

DouglasPontes/2020-Q1-full_tweets

2020-Q1-full_tweets

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

DouglasPontes/
2020-Q1-full_tweets