bert_base_chinese_baidu_fintune

This model is a fine-tuned version of bert-base-chinese on the None dataset. It achieves the following results on the evaluation set:

• Loss: 2.9134
• Mse: 2.9134

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: 3e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Mse
4.2891	0.0	50	4.0176	4.0175
4.0197	0.01	100	3.7198	3.7198
3.9953	0.01	150	3.7285	3.7284
3.6974	0.01	200	4.2507	4.2507
3.6884	0.02	250	3.6316	3.6315
3.5951	0.02	300	3.6354	3.6354
3.5379	0.03	350	3.5250	3.5250
3.6804	0.03	400	3.4025	3.4025
3.3788	0.03	450	3.6585	3.6585
3.864	0.04	500	3.4324	3.4324
3.5062	0.04	550	3.3671	3.3671
3.478	0.04	600	3.5055	3.5055
3.3894	0.05	650	3.3347	3.3347
3.3577	0.05	700	3.3462	3.3462
3.5431	0.05	750	3.5167	3.5167
3.421	0.06	800	3.2970	3.2970
3.407	0.06	850	3.3696	3.3695
3.4202	0.06	900	3.3125	3.3125
3.5096	0.07	950	3.4387	3.4387
3.4338	0.07	1000	3.5653	3.5653
3.6507	0.08	1050	3.6666	3.6667
3.3724	0.08	1100	3.3731	3.3731
3.7244	0.08	1150	3.3666	3.3666
3.3777	0.09	1200	3.6397	3.6397
3.583	0.09	1250	3.3781	3.3780
3.2942	0.09	1300	3.3208	3.3207
3.4335	0.1	1350	3.3797	3.3797
3.2721	0.1	1400	3.3782	3.3782
3.2478	0.1	1450	3.3834	3.3834
3.6509	0.11	1500	3.2751	3.2751
3.5373	0.11	1550	3.3858	3.3858
3.5735	0.11	1600	3.6914	3.6913
3.3937	0.12	1650	3.3257	3.3257
3.1949	0.12	1700	3.3608	3.3608
3.5509	0.13	1750	3.3229	3.3228
3.434	0.13	1800	3.3007	3.3007
3.2915	0.13	1850	3.3351	3.3351
3.2697	0.14	1900	3.2991	3.2991
3.2213	0.14	1950	3.3364	3.3364
3.1428	0.14	2000	3.2597	3.2597
3.1465	0.15	2050	3.2324	3.2324
3.3002	0.15	2100	3.2291	3.2290
3.3223	0.15	2150	3.2819	3.2819
3.3418	0.16	2200	3.4539	3.4539
3.2661	0.16	2250	3.2577	3.2577
3.2665	0.17	2300	3.3346	3.3345
3.1816	0.17	2350	3.2627	3.2627
3.3308	0.17	2400	3.1830	3.1830
3.0341	0.18	2450	3.3091	3.3092
3.1945	0.18	2500	3.2192	3.2192
3.4072	0.18	2550	3.2281	3.2281
3.2343	0.19	2600	3.1747	3.1747
3.1914	0.19	2650	3.2712	3.2712
3.2789	0.19	2700	3.2793	3.2792
3.5793	0.2	2750	3.2033	3.2033
3.069	0.2	2800	3.5477	3.5477
3.2867	0.2	2850	3.2137	3.2137
3.3217	0.21	2900	3.2518	3.2518
3.1865	0.21	2950	3.3086	3.3086
3.1641	0.22	3000	3.2486	3.2486
3.1733	0.22	3050	3.2717	3.2717
3.3107	0.22	3100	3.2439	3.2439
3.2632	0.23	3150	3.2095	3.2095
3.1569	0.23	3200	3.2758	3.2758
3.3872	0.23	3250	3.1989	3.1989
3.1676	0.24	3300	3.1942	3.1942
3.301	0.24	3350	3.2256	3.2256
3.0839	0.24	3400	3.5059	3.5059
3.2125	0.25	3450	3.1671	3.1671
3.2996	0.25	3500	3.1261	3.1261
3.0045	0.25	3550	3.1477	3.1477
3.204	0.26	3600	3.3003	3.3003
3.3212	0.26	3650	3.1440	3.1440
3.0475	0.27	3700	3.1829	3.1829
3.1462	0.27	3750	3.1428	3.1428
3.2983	0.27	3800	3.1720	3.1720
3.5087	0.28	3850	3.1918	3.1918
3.1398	0.28	3900	3.1717	3.1717
3.1668	0.28	3950	3.2359	3.2359
3.2098	0.29	4000	3.1765	3.1765
3.2907	0.29	4050	3.1372	3.1372
3.063	0.29	4100	3.2287	3.2287
3.1269	0.3	4150	3.1292	3.1292
2.8749	0.3	4200	3.2760	3.2761
3.1634	0.31	4250	3.1644	3.1644
3.5689	0.31	4300	3.1634	3.1634
3.1685	0.31	4350	3.2055	3.2055
3.1687	0.32	4400	3.1537	3.1537
3.068	0.32	4450	3.1519	3.1518
3.1029	0.32	4500	3.2265	3.2264
3.3463	0.33	4550	3.1653	3.1653
3.2194	0.33	4600	3.1692	3.1692
3.386	0.33	4650	3.2148	3.2148
3.0511	0.34	4700	3.1837	3.1837
3.2149	0.34	4750	3.2606	3.2606
3.258	0.34	4800	3.1853	3.1853
3.4155	0.35	4850	3.1749	3.1749
2.913	0.35	4900	3.1410	3.1410
3.1222	0.36	4950	3.1347	3.1346
3.2797	0.36	5000	3.1493	3.1493
3.2699	0.36	5050	3.1076	3.1075
3.3319	0.37	5100	3.1395	3.1395
3.0665	0.37	5150	3.1579	3.1579
3.1746	0.37	5200	3.0783	3.0783
3.167	0.38	5250	3.1002	3.1002
3.1945	0.38	5300	3.1255	3.1254
3.1175	0.38	5350	3.2457	3.2457
3.1442	0.39	5400	3.0763	3.0763
3.0234	0.39	5450	3.1150	3.1150
3.2851	0.39	5500	3.1527	3.1526
3.2582	0.4	5550	3.1783	3.1783
3.486	0.4	5600	3.0703	3.0703
3.0174	0.41	5650	3.1628	3.1628
3.0218	0.41	5700	3.0815	3.0815
3.1719	0.41	5750	3.1450	3.1449
3.0538	0.42	5800	3.2821	3.2821
3.089	0.42	5850	3.1103	3.1103
3.2584	0.42	5900	3.0682	3.0682
3.0384	0.43	5950	3.0831	3.0831
3.146	0.43	6000	3.0556	3.0556
3.3227	0.43	6050	3.1558	3.1558
3.084	0.44	6100	3.1062	3.1062
3.035	0.44	6150	3.1382	3.1381
3.2302	0.44	6200	3.4294	3.4294
3.2471	0.45	6250	3.0630	3.0629
3.3483	0.45	6300	3.0820	3.0820
3.1711	0.46	6350	3.1196	3.1196
3.2419	0.46	6400	3.1502	3.1501
3.2064	0.46	6450	3.0777	3.0777
3.2577	0.47	6500	3.1496	3.1496
3.1598	0.47	6550	3.1436	3.1436
3.261	0.47	6600	3.0848	3.0848
3.0999	0.48	6650	3.4262	3.4262
3.2579	0.48	6700	3.1434	3.1434
3.0663	0.48	6750	3.1967	3.1967
2.9269	0.49	6800	3.1421	3.1420
3.0539	0.49	6850	3.1127	3.1127
3.0889	0.5	6900	3.0883	3.0882
3.3546	0.5	6950	3.1240	3.1240
2.7959	0.5	7000	3.1809	3.1809
3.1456	0.51	7050	3.1098	3.1098
3.129	0.51	7100	3.1305	3.1305
3.0578	0.51	7150	3.0595	3.0594
2.9928	0.52	7200	3.2893	3.2894
3.3873	0.52	7250	3.0535	3.0535
3.276	0.52	7300	3.1102	3.1101
3.0081	0.53	7350	3.0800	3.0799
2.995	0.53	7400	3.0763	3.0762
3.0534	0.53	7450	3.1923	3.1922
2.9008	0.54	7500	3.1613	3.1613
3.1102	0.54	7550	3.1667	3.1667
3.1981	0.55	7600	3.0901	3.0901
3.1943	0.55	7650	3.1479	3.1479
2.9393	0.55	7700	3.0897	3.0897
3.4017	0.56	7750	3.1133	3.1133
3.1755	0.56	7800	3.1046	3.1045
3.2098	0.56	7850	3.1901	3.1901
3.0473	0.57	7900	3.0407	3.0407
3.1164	0.57	7950	3.0538	3.0538
3.0977	0.57	8000	3.0916	3.0916
3.1668	0.58	8050	3.0511	3.0511
3.1759	0.58	8100	3.0570	3.0569
3.0314	0.58	8150	3.0392	3.0391
3.1754	0.59	8200	3.0931	3.0931
3.1641	0.59	8250	3.0616	3.0616
3.1117	0.6	8300	3.0858	3.0858
3.0428	0.6	8350	3.3001	3.3001
3.2059	0.6	8400	3.1211	3.1211
3.1379	0.61	8450	3.1142	3.1142
2.6985	0.61	8500	3.0227	3.0227
3.1372	0.61	8550	3.3303	3.3303
3.133	0.62	8600	3.0319	3.0319
2.8701	0.62	8650	3.0984	3.0984
3.3546	0.62	8700	3.0341	3.0340
3.3581	0.63	8750	3.0209	3.0208
3.2742	0.63	8800	3.1695	3.1695
2.9777	0.64	8850	3.1243	3.1243
3.2559	0.64	8900	3.0289	3.0289
2.8806	0.64	8950	3.0622	3.0622
3.0749	0.65	9000	3.0341	3.0341
3.0466	0.65	9050	3.0805	3.0805
2.9984	0.65	9100	3.0313	3.0312
3.203	0.66	9150	3.0184	3.0183
3.2582	0.66	9200	3.1197	3.1197
3.2952	0.66	9250	3.0834	3.0834
2.9485	0.67	9300	3.0659	3.0659
3.0277	0.67	9350	3.0454	3.0454
3.2054	0.67	9400	3.1008	3.1008
3.0935	0.68	9450	3.0649	3.0648
3.0175	0.68	9500	3.0549	3.0549
3.1301	0.69	9550	3.0076	3.0076
3.0053	0.69	9600	3.0320	3.0319
2.9718	0.69	9650	3.0270	3.0270
3.0023	0.7	9700	3.0470	3.0469
3.3893	0.7	9750	2.9923	2.9922
3.0126	0.7	9800	3.1265	3.1265
2.7614	0.71	9850	3.2194	3.2194
3.1488	0.71	9900	3.0394	3.0394
3.0751	0.71	9950	3.0037	3.0037
2.6901	0.72	10000	3.0517	3.0517
3.1097	0.72	10050	3.0385	3.0385
2.9786	0.72	10100	3.0478	3.0478
3.0759	0.73	10150	3.0663	3.0663
3.1498	0.73	10200	3.0112	3.0112
3.1841	0.74	10250	3.0059	3.0059
2.8827	0.74	10300	3.1028	3.1028
3.0948	0.74	10350	3.0770	3.0770
3.1116	0.75	10400	3.1307	3.1306
2.8361	0.75	10450	3.0373	3.0373
3.2783	0.75	10500	2.9874	2.9874
2.8844	0.76	10550	3.0150	3.0150
2.9918	0.76	10600	3.0176	3.0175
3.1552	0.76	10650	2.9842	2.9841
2.8834	0.77	10700	3.0438	3.0437
2.9602	0.77	10750	3.0263	3.0262
3.215	0.78	10800	2.9959	2.9959
3.172	0.78	10850	3.0018	3.0018
2.7982	0.78	10900	2.9811	2.9811
2.99	0.79	10950	3.0473	3.0472
3.2533	0.79	11000	2.9874	2.9873
3.0024	0.79	11050	2.9936	2.9935
3.0641	0.8	11100	3.0023	3.0022
2.834	0.8	11150	3.0665	3.0665
3.5	0.8	11200	3.0045	3.0044
2.9229	0.81	11250	2.9972	2.9972
3.1083	0.81	11300	3.0198	3.0198
3.1141	0.81	11350	3.0926	3.0926
3.2897	0.82	11400	3.0195	3.0195
2.703	0.82	11450	2.9642	2.9642
3.2053	0.83	11500	3.0739	3.0739
3.0592	0.83	11550	3.0547	3.0547
2.7905	0.83	11600	3.0112	3.0112
3.0521	0.84	11650	2.9676	2.9676
2.8807	0.84	11700	2.9737	2.9737
3.212	0.84	11750	3.0579	3.0578
3.1624	0.85	11800	3.0113	3.0112
3.0013	0.85	11850	3.0262	3.0262
3.1247	0.85	11900	3.0005	3.0005
3.122	0.86	11950	3.0288	3.0288
2.9088	0.86	12000	3.0101	3.0101
3.3433	0.86	12050	3.0417	3.0417
3.1722	0.87	12100	2.9808	2.9807
3.0472	0.87	12150	2.9896	2.9896
2.8991	0.88	12200	2.9739	2.9738
2.8017	0.88	12250	3.1197	3.1197
3.1467	0.88	12300	2.9484	2.9483
3.0622	0.89	12350	3.0068	3.0068
2.7503	0.89	12400	3.0082	3.0082
2.9746	0.89	12450	3.0171	3.0171
3.0332	0.9	12500	3.0219	3.0219
2.9461	0.9	12550	3.0852	3.0852
3.1592	0.9	12600	2.9739	2.9739
3.1065	0.91	12650	2.9762	2.9762
2.9471	0.91	12700	2.9900	2.9900
3.0888	0.92	12750	2.9958	2.9958
3.0276	0.92	12800	2.9635	2.9634
3.3018	0.92	12850	2.9799	2.9799
3.0144	0.93	12900	3.0390	3.0390
3.123	0.93	12950	3.0114	3.0114
2.9762	0.93	13000	2.9466	2.9466
3.0882	0.94	13050	2.9648	2.9648
3.378	0.94	13100	2.9714	2.9714
2.9257	0.94	13150	2.9608	2.9607
3.1253	0.95	13200	2.9670	2.9670
3.0435	0.95	13250	2.9772	2.9772
3.1933	0.95	13300	2.9668	2.9667
2.6627	0.96	13350	2.9485	2.9485
2.8993	0.96	13400	2.9604	2.9604
3.0717	0.97	13450	2.9680	2.9680
2.9808	0.97	13500	3.0079	3.0079
3.1127	0.97	13550	3.0293	3.0292
2.7839	0.98	13600	3.0223	3.0222
3.0486	0.98	13650	2.9962	2.9962
2.9194	0.98	13700	3.0340	3.0340
3.0708	0.99	13750	2.9454	2.9454
2.8585	0.99	13800	3.0066	3.0065
2.9663	0.99	13850	2.9561	2.9561
3.1141	1.0	13900	2.9465	2.9465
2.9909	1.0	13950	2.9614	2.9613
2.8155	1.0	14000	2.9983	2.9983
2.676	1.01	14050	2.9545	2.9545
3.0067	1.01	14100	3.0463	3.0463
2.7865	1.02	14150	3.1286	3.1285
2.7287	1.02	14200	3.0271	3.0270
2.4092	1.02	14250	3.0883	3.0883
2.6929	1.03	14300	2.9681	2.9680
2.7634	1.03	14350	2.9687	2.9686
2.8261	1.03	14400	3.0169	3.0169
2.7826	1.04	14450	2.9896	2.9896
2.5205	1.04	14500	3.0000	3.0000
2.5125	1.04	14550	3.2051	3.2051
2.7654	1.05	14600	2.9598	2.9598
2.7537	1.05	14650	3.0330	3.0330
2.8008	1.05	14700	2.9685	2.9685
2.7475	1.06	14750	2.9752	2.9752
2.9336	1.06	14800	2.9771	2.9771
2.7198	1.07	14850	2.9437	2.9437
2.8061	1.07	14900	3.0164	3.0164
2.6694	1.07	14950	3.0257	3.0257
3.0206	1.08	15000	2.9708	2.9708
2.5526	1.08	15050	3.0267	3.0267
2.5243	1.08	15100	2.9703	2.9702
2.5846	1.09	15150	2.9967	2.9967
2.7397	1.09	15200	3.0103	3.0103
2.673	1.09	15250	2.9754	2.9754
2.5084	1.1	15300	3.0346	3.0345
2.4855	1.1	15350	2.9458	2.9457
2.7313	1.11	15400	2.9859	2.9858
2.7006	1.11	15450	3.0760	3.0759
2.7244	1.11	15500	3.0000	3.0000
2.4614	1.12	15550	3.0309	3.0309
2.4961	1.12	15600	3.0103	3.0103
2.768	1.12	15650	2.9935	2.9935
2.7499	1.13	15700	3.0056	3.0056
2.653	1.13	15750	3.0597	3.0597
2.6518	1.13	15800	3.0372	3.0372
2.7115	1.14	15850	2.9719	2.9719
2.7183	1.14	15900	3.0150	3.0150
2.642	1.14	15950	2.9677	2.9676
2.4724	1.15	16000	3.1429	3.1429
2.5061	1.15	16050	3.0118	3.0118
2.6537	1.16	16100	2.9486	2.9485
2.5527	1.16	16150	2.9290	2.9289
2.5993	1.16	16200	3.0312	3.0312
2.5689	1.17	16250	2.9628	2.9628
2.6791	1.17	16300	2.9799	2.9799
2.5362	1.17	16350	2.9344	2.9344
2.722	1.18	16400	2.9889	2.9889
2.6466	1.18	16450	3.0463	3.0463
2.7251	1.18	16500	2.9908	2.9908
2.6939	1.19	16550	3.0059	3.0059
2.5142	1.19	16600	3.1051	3.1050
2.708	1.19	16650	3.0247	3.0246
2.8829	1.2	16700	3.0766	3.0766
2.4804	1.2	16750	2.9606	2.9606
2.7648	1.21	16800	3.0024	3.0024
2.6951	1.21	16850	2.9377	2.9377
2.6268	1.21	16900	2.9665	2.9665
2.4565	1.22	16950	2.9571	2.9571
2.4351	1.22	17000	2.9667	2.9667
2.5413	1.22	17050	2.9858	2.9857
2.4026	1.23	17100	2.9627	2.9627
2.475	1.23	17150	3.0614	3.0613
2.6409	1.23	17200	2.9948	2.9947
2.4096	1.24	17250	2.9809	2.9809
2.9013	1.24	17300	2.9059	2.9059
2.5439	1.25	17350	3.0579	3.0579
2.7954	1.25	17400	2.9680	2.9680
2.5737	1.25	17450	2.9070	2.9070
2.8598	1.26	17500	2.9365	2.9364
2.6169	1.26	17550	2.9778	2.9777
2.5259	1.26	17600	2.9682	2.9681
2.8575	1.27	17650	2.9945	2.9945
2.7421	1.27	17700	2.9520	2.9520
2.8372	1.27	17750	2.9436	2.9435
2.5107	1.28	17800	2.9719	2.9718
2.6528	1.28	17850	3.0114	3.0114
2.5169	1.28	17900	2.9163	2.9163
2.5384	1.29	17950	2.9369	2.9369
2.4932	1.29	18000	2.9385	2.9384
2.654	1.3	18050	2.9273	2.9273
2.5108	1.3	18100	2.9197	2.9197
2.6425	1.3	18150	2.9047	2.9047
2.5097	1.31	18200	2.8998	2.8998
2.6153	1.31	18250	2.9400	2.9399
2.6642	1.31	18300	2.9071	2.9071
2.5172	1.32	18350	2.9538	2.9537
2.6641	1.32	18400	2.9670	2.9670
2.667	1.32	18450	2.9586	2.9586
2.3798	1.33	18500	2.9442	2.9442
2.7429	1.33	18550	2.9354	2.9354
2.6313	1.33	18600	2.9349	2.9349
2.7297	1.34	18650	2.9436	2.9436
2.4944	1.34	18700	2.9431	2.9431
2.5849	1.35	18750	2.9068	2.9068
2.4072	1.35	18800	2.9049	2.9049
2.5155	1.35	18850	2.9386	2.9386
2.4623	1.36	18900	2.9390	2.9390
2.3734	1.36	18950	2.8948	2.8948
2.662	1.36	19000	3.0272	3.0272
2.6445	1.37	19050	3.0893	3.0893
2.5997	1.37	19100	2.9809	2.9809
2.7098	1.37	19150	2.9353	2.9353
2.7256	1.38	19200	2.9524	2.9523
2.7286	1.38	19250	3.0198	3.0198
2.6852	1.39	19300	2.9169	2.9169
2.6173	1.39	19350	2.9124	2.9124
2.9245	1.39	19400	2.9010	2.9010
2.4449	1.4	19450	2.9271	2.9271
2.7729	1.4	19500	2.9354	2.9354
2.5422	1.4	19550	2.9942	2.9942
2.8516	1.41	19600	2.9525	2.9525
2.6338	1.41	19650	2.9009	2.9009
2.536	1.41	19700	2.8967	2.8967
2.6251	1.42	19750	2.9858	2.9858
2.6675	1.42	19800	2.9368	2.9367
2.649	1.42	19850	2.9188	2.9187
2.4321	1.43	19900	2.9024	2.9024
2.5635	1.43	19950	2.9593	2.9592
2.7008	1.44	20000	2.9312	2.9312
2.3847	1.44	20050	2.9469	2.9469
2.5795	1.44	20100	2.9610	2.9610
2.5448	1.45	20150	2.9250	2.9249
2.4307	1.45	20200	2.8984	2.8984
2.603	1.45	20250	2.9128	2.9127
2.4792	1.46	20300	2.9316	2.9315
2.5079	1.46	20350	2.9318	2.9318
2.4144	1.46	20400	2.9658	2.9657
2.4941	1.47	20450	2.9321	2.9321
2.6389	1.47	20500	2.9407	2.9406
2.6555	1.47	20550	2.9680	2.9679
2.4947	1.48	20600	2.8995	2.8995
2.8275	1.48	20650	2.9178	2.9178
2.7041	1.49	20700	2.9182	2.9182
2.3485	1.49	20750	2.9254	2.9254
2.4669	1.49	20800	2.9146	2.9146
2.7119	1.5	20850	2.9105	2.9105
2.5042	1.5	20900	2.9439	2.9439
2.6387	1.5	20950	2.9054	2.9054
2.7571	1.51	21000	2.8993	2.8992
2.6901	1.51	21050	2.9055	2.9055
2.5939	1.51	21100	2.9496	2.9496
2.6441	1.52	21150	2.9458	2.9458
2.73	1.52	21200	2.9073	2.9073
2.5875	1.53	21250	2.9283	2.9283
2.6216	1.53	21300	2.9595	2.9594
2.777	1.53	21350	2.9612	2.9612
2.7403	1.54	21400	2.8779	2.8778
2.5636	1.54	21450	2.9410	2.9409
2.4265	1.54	21500	2.9706	2.9706
2.6707	1.55	21550	2.9196	2.9196
2.3088	1.55	21600	2.9238	2.9237
2.7564	1.55	21650	2.9096	2.9096
2.6355	1.56	21700	2.9042	2.9042
2.425	1.56	21750	2.9651	2.9651
2.3169	1.56	21800	2.9371	2.9371
2.6283	1.57	21850	2.9201	2.9201
2.4333	1.57	21900	3.0037	3.0037
2.5661	1.58	21950	2.9179	2.9178
2.58	1.58	22000	2.9419	2.9419
2.6451	1.58	22050	2.9683	2.9682
2.4686	1.59	22100	2.9073	2.9073
2.4795	1.59	22150	2.9364	2.9364
2.6442	1.59	22200	2.9521	2.9520
2.4085	1.6	22250	2.9353	2.9352
2.4595	1.6	22300	2.9340	2.9340
2.5705	1.6	22350	2.9283	2.9283
2.4189	1.61	22400	2.9017	2.9016
2.5823	1.61	22450	2.9032	2.9032
2.5402	1.61	22500	2.9039	2.9038
2.8166	1.62	22550	2.8849	2.8849
2.6202	1.62	22600	2.8800	2.8800
2.584	1.63	22650	2.8750	2.8750
2.3816	1.63	22700	2.9109	2.9108
2.5496	1.63	22750	2.9024	2.9024
2.5379	1.64	22800	2.8798	2.8798
2.8131	1.64	22850	2.8656	2.8656
2.2938	1.64	22900	2.9004	2.9004
2.6783	1.65	22950	2.8878	2.8878
2.5324	1.65	23000	2.8982	2.8981
2.6519	1.65	23050	2.8990	2.8990
2.8409	1.66	23100	2.9316	2.9316
2.6925	1.66	23150	2.9169	2.9168
2.5419	1.66	23200	2.9039	2.9039
2.3325	1.67	23250	2.9207	2.9207
2.6392	1.67	23300	2.9194	2.9193
2.8263	1.68	23350	2.9086	2.9085
2.7376	1.68	23400	2.9024	2.9024
2.2401	1.68	23450	2.9111	2.9110
2.4786	1.69	23500	2.9104	2.9104
2.55	1.69	23550	2.9199	2.9199
2.8087	1.69	23600	2.9298	2.9298
2.6732	1.7	23650	2.9338	2.9338
2.4693	1.7	23700	2.9224	2.9224
2.5044	1.7	23750	2.9163	2.9162
2.5339	1.71	23800	2.9201	2.9201
2.6954	1.71	23850	2.9250	2.9250
2.4067	1.72	23900	2.9298	2.9298
2.642	1.72	23950	2.8989	2.8989
2.5598	1.72	24000	2.9036	2.9035
2.3665	1.73	24050	2.9076	2.9075
2.702	1.73	24100	2.9168	2.9167
2.5716	1.73	24150	2.9149	2.9149
2.5707	1.74	24200	2.9051	2.9051
2.5379	1.74	24250	2.9431	2.9431
2.3297	1.74	24300	2.9746	2.9746
2.405	1.75	24350	2.9450	2.9449
2.7137	1.75	24400	2.9306	2.9306
2.3818	1.75	24450	2.9424	2.9423
2.2058	1.76	24500	2.9433	2.9433
2.2247	1.76	24550	2.9475	2.9474
2.5951	1.77	24600	2.9248	2.9247
2.6076	1.77	24650	2.9035	2.9034
2.4384	1.77	24700	2.9169	2.9169
2.5674	1.78	24750	2.9230	2.9230
2.3697	1.78	24800	2.9288	2.9287
2.4873	1.78	24850	2.9343	2.9342
2.4828	1.79	24900	2.9140	2.9140
2.4045	1.79	24950	2.9132	2.9132
2.4529	1.79	25000	2.9224	2.9224
2.425	1.8	25050	2.9152	2.9152
2.4542	1.8	25100	2.9062	2.9062
2.5876	1.8	25150	2.9111	2.9111
2.537	1.81	25200	2.9082	2.9081
2.487	1.81	25250	2.9120	2.9120
2.3972	1.82	25300	2.9032	2.9032
2.3996	1.82	25350	2.8937	2.8937
2.5223	1.82	25400	2.8976	2.8975
2.5235	1.83	25450	2.9135	2.9135
2.5024	1.83	25500	2.9238	2.9238
2.6154	1.83	25550	2.9292	2.9291
2.6438	1.84	25600	2.9280	2.9280
2.5625	1.84	25650	2.9254	2.9253
2.667	1.84	25700	2.9235	2.9234
2.7495	1.85	25750	2.9195	2.9195
2.6583	1.85	25800	2.9210	2.9210
2.6855	1.86	25850	2.9162	2.9162
2.4995	1.86	25900	2.9150	2.9149
2.6508	1.86	25950	2.9228	2.9228
2.6263	1.87	26000	2.9254	2.9253
2.5796	1.87	26050	2.9271	2.9270
2.4272	1.87	26100	2.9225	2.9225
2.5424	1.88	26150	2.9218	2.9217
2.6146	1.88	26200	2.9216	2.9216
2.3928	1.88	26250	2.9184	2.9184
2.7237	1.89	26300	2.9169	2.9169
2.4522	1.89	26350	2.9167	2.9167
2.65	1.89	26400	2.9186	2.9185
2.3969	1.9	26450	2.9151	2.9150
2.6054	1.9	26500	2.9185	2.9185
2.6169	1.91	26550	2.9179	2.9179
2.6473	1.91	26600	2.9148	2.9148
2.7241	1.91	26650	2.9127	2.9127
2.5228	1.92	26700	2.9122	2.9122
2.2797	1.92	26750	2.9116	2.9116
2.3311	1.92	26800	2.9096	2.9096
2.4659	1.93	26850	2.9097	2.9097
2.6423	1.93	26900	2.9115	2.9114
2.6203	1.93	26950	2.9130	2.9130
2.5754	1.94	27000	2.9125	2.9125
2.2694	1.94	27050	2.9122	2.9121
2.4308	1.94	27100	2.9127	2.9126
2.3289	1.95	27150	2.9129	2.9128
2.6457	1.95	27200	2.9128	2.9128
2.4722	1.96	27250	2.9126	2.9126
2.5979	1.96	27300	2.9133	2.9133
2.5693	1.96	27350	2.9137	2.9137
2.6261	1.97	27400	2.9134	2.9134
2.7006	1.97	27450	2.9136	2.9135
2.6482	1.97	27500	2.9134	2.9134
2.6639	1.98	27550	2.9134	2.9134
2.6761	1.98	27600	2.9133	2.9133
2.4477	1.98	27650	2.9134	2.9134
2.4656	1.99	27700	2.9134	2.9134
2.7268	1.99	27750	2.9134	2.9134
2.4972	2.0	27800	2.9134	2.9134
2.517	2.0	27850	2.9134	2.9134

Framework versions

• Transformers 4.34.0
• Pytorch 2.0.1
• Datasets 2.14.5
• Tokenizers 0.14.1