learning_rate: 5e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
2.3267	0.0085	100	1.9629
2.0179	0.0171	200	1.7591
1.831	0.0256	300	1.6275
1.7046	0.0342	400	1.5287
1.7428	0.0427	500	1.4256
1.5482	0.0513	600	1.3708
1.6759	0.0598	700	1.3091
1.3827	0.0684	800	1.2703
1.3831	0.0769	900	1.2452
1.3291	0.0855	1000	1.2163
1.4892	0.0940	1100	1.1667
1.252	0.1026	1200	1.1614
1.2592	0.1111	1300	1.1390
1.24	0.1197	1400	1.1300
1.2874	0.1282	1500	1.1025
1.1944	0.1368	1600	1.1121
1.1848	0.1453	1700	1.0787
1.1624	0.1538	1800	1.0771
1.1606	0.1624	1900	1.0543
1.1327	0.1709	2000	1.0570
1.1515	0.1795	2100	1.0289
1.1878	0.1880	2200	1.0197
1.1357	0.1966	2300	1.0111
1.1731	0.2051	2400	1.0004
1.2001	0.2137	2500	0.9900
1.1556	0.2222	2600	0.9905
1.0853	0.2308	2700	0.9713
1.0645	0.2393	2800	0.9911
1.067	0.2479	2900	0.9769
1.0773	0.2564	3000	0.9609
1.0611	0.2650	3100	0.9627
1.0406	0.2735	3200	0.9590
1.0593	0.2821	3300	0.9496
1.0488	0.2906	3400	0.9362
1.0286	0.2991	3500	0.9402
1.0386	0.3077	3600	0.9375
1.0311	0.3162	3700	0.9409
1.0082	0.3248	3800	0.9225
1.0254	0.3333	3900	0.9102
1.0138	0.3419	4000	0.9152
1.027	0.3504	4100	0.9093
1.0049	0.3590	4200	0.9113
1.0084	0.3675	4300	0.8997
0.9844	0.3761	4400	0.8973
1.0041	0.3846	4500	0.8924
1.0071	0.3932	4600	0.8991
0.9672	0.4017	4700	0.8899
0.9882	0.4103	4800	0.8971
0.9994	0.4188	4900	0.8927
0.9825	0.4274	5000	0.8883
0.9643	0.4359	5100	0.8765
0.987	0.4444	5200	0.8773
0.977	0.4530	5300	0.8685
0.9527	0.4615	5400	0.8775
0.9825	0.4701	5500	0.8643
0.97	0.4786	5600	0.8657
0.9454	0.4872	5700	0.8697
0.946	0.4957	5800	0.8585
0.9617	0.5043	5900	0.8570
0.9469	0.5128	6000	0.8540
0.9455	0.5214	6100	0.8465
0.9516	0.5299	6200	0.8444
0.9383	0.5385	6300	0.8491
0.9211	0.5470	6400	0.8485
0.9347	0.5556	6500	0.8425
0.938	0.5641	6600	0.8475
0.9274	0.5726	6700	0.8405
0.9257	0.5812	6800	0.8435
0.9161	0.5897	6900	0.8314
0.9268	0.5983	7000	0.8335
0.9321	0.6068	7100	0.8310
0.9168	0.6154	7200	0.8340
0.9141	0.6239	7300	0.8271
0.9077	0.6325	7400	0.8236
0.9189	0.6410	7500	0.8210
0.8914	0.6496	7600	0.8214
0.9012	0.6581	7700	0.8241
0.8942	0.6667	7800	0.8180
0.8981	0.6752	7900	0.8211
0.8997	0.6838	8000	0.8163
0.909	0.6923	8100	0.8156
0.8908	0.7009	8200	0.8193
0.8938	0.7094	8300	0.8198
0.912	0.7179	8400	0.8087
0.8627	0.7265	8500	0.8064
0.8917	0.7350	8600	0.8048
0.8924	0.7436	8700	0.8077
0.9046	0.7521	8800	0.8110
0.8906	0.7607	8900	0.8074
0.8838	0.7692	9000	0.7980
0.8928	0.7778	9100	0.8033
0.8911	0.7863	9200	0.8047
0.9032	0.7949	9300	0.8000
0.8749	0.8034	9400	0.8011
0.8782	0.8120	9500	0.7997
0.8711	0.8205	9600	0.7930
0.8996	0.8291	9700	0.7918
0.8957	0.8376	9800	0.7973
0.893	0.8462	9900	0.7901
0.861	0.8547	10000	0.7907
0.8679	0.8632	10100	0.7889
0.8671	0.8718	10200	0.7882
0.8719	0.8803	10300	0.7876
0.8798	0.8889	10400	0.7817
0.8753	0.8974	10500	0.7832
0.8583	0.9060	10600	0.7863
0.8586	0.9145	10700	0.7856
0.8585	0.9231	10800	0.7836
0.8626	0.9316	10900	0.7839
0.8549	0.9402	11000	0.7844
0.8791	0.9487	11100	0.7825
0.86	0.9573	11200	0.7777
0.85	0.9658	11300	0.7744
0.8609	0.9744	11400	0.7832
0.8424	0.9829	11500	0.7813
0.8628	0.9915	11600	0.7712
0.8572	1.0	11700	0.7776
0.8584	1.0085	11800	0.7719
0.828	1.0171	11900	0.7729
0.8605	1.0256	12000	0.7716
0.8294	1.0342	12100	0.7733
0.8371	1.0427	12200	0.7654
0.8464	1.0513	12300	0.7690
0.8388	1.0598	12400	0.7672
0.8545	1.0684	12500	0.7696
0.8369	1.0769	12600	0.7625
0.853	1.0855	12700	0.7625
0.839	1.0940	12800	0.7638
0.8383	1.1026	12900	0.7604
0.8434	1.1111	13000	0.7689
0.8449	1.1197	13100	0.7633
0.8581	1.1282	13200	0.7584
0.8313	1.1368	13300	0.7590
0.8475	1.1453	13400	0.7600
0.8231	1.1538	13500	0.7591
0.8294	1.1624	13600	0.7565
0.8068	1.1709	13700	0.7579
0.8467	1.1795	13800	0.7592
0.8329	1.1880	13900	0.7581
0.8255	1.1966	14000	0.7539
0.8353	1.2051	14100	0.7614
0.8233	1.2137	14200	0.7548
0.8551	1.2222	14300	0.7493
0.8267	1.2308	14400	0.7533
0.8366	1.2393	14500	0.7512
0.8242	1.2479	14600	0.7453
0.8414	1.2564	14700	0.7496
0.8375	1.2650	14800	0.7490
0.8544	1.2735	14900	0.7522
0.8444	1.2821	15000	0.7476
0.82	1.2906	15100	0.7489
0.8174	1.2991	15200	0.7499
0.8187	1.3077	15300	0.7476
0.8277	1.3162	15400	0.7426
0.8227	1.3248	15500	0.7454
0.8222	1.3333	15600	0.7457
0.8147	1.3419	15700	0.7402
0.8165	1.3504	15800	0.7399
0.8203	1.3590	15900	0.7423
0.8223	1.3675	16000	0.7436
0.8227	1.3761	16100	0.7437
0.8149	1.3846	16200	0.7405
0.8035	1.3932	16300	0.7397
0.8217	1.4017	16400	0.7378
0.8057	1.4103	16500	0.7418
0.8091	1.4188	16600	0.7479
0.811	1.4274	16700	0.7361
0.8214	1.4359	16800	0.7368
0.8231	1.4444	16900	0.7370
0.8024	1.4530	17000	0.7351
0.8205	1.4615	17100	0.7391
0.8006	1.4701	17200	0.7382
0.8047	1.4786	17300	0.7344
0.7927	1.4872	17400	0.7337
0.8115	1.4957	17500	0.7330
0.8108	1.5043	17600	0.7351
0.7982	1.5128	17700	0.7364
0.821	1.5214	17800	0.7326
0.7849	1.5299	17900	0.7277
0.8058	1.5385	18000	0.7284
0.8154	1.5470	18100	0.7303
0.8199	1.5556	18200	0.7342
0.8146	1.5641	18300	0.7274
0.8004	1.5726	18400	0.7294
0.8061	1.5812	18500	0.7308
0.8081	1.5897	18600	0.7254
0.7907	1.5983	18700	0.7293
0.8093	1.6068	18800	0.7304
0.7959	1.6154	18900	0.7257
0.7905	1.6239	19000	0.7249
0.8009	1.6325	19100	0.7228
0.7964	1.6410	19200	0.7268
0.7851	1.6496	19300	0.7275
0.7882	1.6581	19400	0.7231
0.8029	1.6667	19500	0.7241
0.7821	1.6752	19600	0.7206
0.7779	1.6838	19700	0.7244
0.8038	1.6923	19800	0.7229
0.7965	1.7009	19900	0.7234
0.8075	1.7094	20000	0.7244
0.7901	1.7179	20100	0.7223
0.7817	1.7265	20200	0.7212
0.8065	1.7350	20300	0.7240
0.791	1.7436	20400	0.7190
0.8007	1.7521	20500	0.7173
0.7936	1.7607	20600	0.7195
0.7983	1.7692	20700	0.7192
0.802	1.7778	20800	0.7177
0.8083	1.7863	20900	0.7195
0.7753	1.7949	21000	0.7179
0.8003	1.8034	21100	0.7188
0.7912	1.8120	21200	0.7194
0.7641	1.8205	21300	0.7190
0.7941	1.8291	21400	0.7192
0.8073	1.8376	21500	0.7174
0.789	1.8462	21600	0.7177
0.7816	1.8547	21700	0.7156
0.784	1.8632	21800	0.7182
0.7758	1.8718	21900	0.7152
0.7668	1.8803	22000	0.7179
0.7873	1.8889	22100	0.7155
0.7835	1.8974	22200	0.7151
0.7857	1.9060	22300	0.7131
0.7847	1.9145	22400	0.7128
0.7866	1.9231	22500	0.7118
0.8015	1.9316	22600	0.7104
0.7802	1.9402	22700	0.7139
0.788	1.9487	22800	0.7113
0.7813	1.9573	22900	0.7134
0.768	1.9658	23000	0.7117
0.7963	1.9744	23100	0.7113
0.7598	1.9829	23200	0.7117
0.7892	1.9915	23300	0.7104
0.769	2.0	23400	0.7138
0.7943	2.0085	23500	0.7099
0.7883	2.0171	23600	0.7092
0.77	2.0256	23700	0.7122
0.7731	2.0342	23800	0.7102
0.7842	2.0427	23900	0.7072
0.7737	2.0513	24000	0.7097
0.7776	2.0598	24100	0.7121
0.763	2.0684	24200	0.7107
0.7835	2.0769	24300	0.7074
0.7834	2.0855	24400	0.7081
0.7823	2.0940	24500	0.7064
0.7809	2.1026	24600	0.7089
0.7764	2.1111	24700	0.7073
0.7756	2.1197	24800	0.7094
0.7605	2.1282	24900	0.7064
0.7649	2.1368	25000	0.7051
0.7724	2.1453	25100	0.7058
0.7506	2.1538	25200	0.7060
0.7682	2.1624	25300	0.7061
0.7797	2.1709	25400	0.7086
0.7712	2.1795	25500	0.7051
0.7538	2.1880	25600	0.7073
0.7748	2.1966	25700	0.7043
0.7687	2.2051	25800	0.7061
0.7813	2.2137	25900	0.7049
0.7553	2.2222	26000	0.7043
0.7655	2.2308	26100	0.7059
0.7795	2.2393	26200	0.7048
0.7788	2.2479	26300	0.7069
0.7999	2.2564	26400	0.7034
0.7841	2.2650	26500	0.7034
0.7777	2.2735	26600	0.7007
0.7755	2.2821	26700	0.7021
0.7761	2.2906	26800	0.7048
0.7844	2.2991	26900	0.7043
0.7724	2.3077	27000	0.7041
0.7784	2.3162	27100	0.7045
0.769	2.3248	27200	0.7013
0.7606	2.3333	27300	0.7028
0.7801	2.3419	27400	0.7015
0.7731	2.3504	27500	0.7023
0.7534	2.3590	27600	0.7009
0.7628	2.3675	27700	0.7006
0.7614	2.3761	27800	0.7002
0.7508	2.3846	27900	0.7017
0.7667	2.3932	28000	0.7021
0.7782	2.4017	28100	0.7016
0.7637	2.4103	28200	0.7003
0.7508	2.4188	28300	0.6993
0.7764	2.4274	28400	0.6995
0.7614	2.4359	28500	0.7035
0.7724	2.4444	28600	0.6979
0.7631	2.4530	28700	0.6992
0.778	2.4615	28800	0.6996
0.7612	2.4701	28900	0.6993
0.7767	2.4786	29000	0.6965
0.7543	2.4872	29100	0.6993
0.7781	2.4957	29200	0.6974
0.7747	2.5043	29300	0.6978
0.7715	2.5128	29400	0.6973
0.7719	2.5214	29500	0.6996
0.7493	2.5299	29600	0.6984
0.7718	2.5385	29700	0.7002
0.7701	2.5470	29800	0.6993
0.7747	2.5556	29900	0.6965
0.778	2.5641	30000	0.7004
0.7506	2.5726	30100	0.6994
0.7429	2.5812	30200	0.6987
0.7605	2.5897	30300	0.6978
0.7632	2.5983	30400	0.7002
0.7532	2.6068	30500	0.6996
0.7725	2.6154	30600	0.6970
0.7528	2.6239	30700	0.6969
0.7692	2.6325	30800	0.6967
0.7806	2.6410	30900	0.6961
0.7779	2.6496	31000	0.6964
0.7606	2.6581	31100	0.6972
0.7438	2.6667	31200	0.6958
0.7687	2.6752	31300	0.6952
0.7429	2.6838	31400	0.6964
0.7737	2.6923	31500	0.6968
0.7559	2.7009	31600	0.6953
0.764	2.7094	31700	0.6955
0.7669	2.7179	31800	0.6959
0.7793	2.7265	31900	0.6949
0.7676	2.7350	32000	0.6958
0.7611	2.7436	32100	0.6951
0.7945	2.7521	32200	0.6935
0.7597	2.7607	32300	0.6942
0.7653	2.7692	32400	0.6951
0.7427	2.7778	32500	0.6952
0.7886	2.7863	32600	0.6937
0.7571	2.7949	32700	0.6954
0.7546	2.8034	32800	0.6961
0.7696	2.8120	32900	0.6944
0.7579	2.8205	33000	0.6943
0.7723	2.8291	33100	0.6943
0.7667	2.8376	33200	0.6945
0.7665	2.8462	33300	0.6941
0.7537	2.8547	33400	0.6944
0.7446	2.8632	33500	0.6952
0.7619	2.8718	33600	0.6942
0.7628	2.8803	33700	0.6946
0.7692	2.8889	33800	0.6946
0.7517	2.8974	33900	0.6946
0.747	2.9060	34000	0.6947
0.7682	2.9145	34100	0.6945
0.7541	2.9231	34200	0.6942
0.7539	2.9316	34300	0.6947
0.7733	2.9402	34400	0.6945
0.77	2.9487	34500	0.6942
0.7589	2.9573	34600	0.6943
0.7552	2.9658	34700	0.6944
0.7587	2.9744	34800	0.6943
0.7687	2.9829	34900	0.6943
0.7518	2.9915	35000	0.6944
0.7695	3.0	35100	0.6943

Framework versions

Transformers 4.52.3
Pytorch 2.7.0+cu128
Datasets 3.6.0
Tokenizers 0.21.1

kienhoang123/t5-vi-instruct-hf

t5-vi-instruct-hf

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

kienhoang123/
t5-vi-instruct-hf