learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 8
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: 15
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
2.5071	0.0978	100	2.3866
1.924	0.1957	200	1.7508
1.3181	0.2935	300	1.4820
1.4516	0.3914	400	1.3604
1.3453	0.4892	500	1.2847
1.2216	0.5871	600	1.2377
1.5336	0.6849	700	1.2089
1.1803	0.7828	800	1.1853
1.352	0.8806	900	1.1659
1.25	0.9785	1000	1.1453
1.1877	1.0763	1100	1.1323
1.1111	1.1742	1200	1.1171
1.2295	1.2720	1300	1.1074
1.1602	1.3699	1400	1.0963
1.0951	1.4677	1500	1.0852
1.1102	1.5656	1600	1.0757
0.9648	1.6634	1700	1.0671
0.9534	1.7613	1800	1.0591
1.1413	1.8591	1900	1.0526
1.1051	1.9569	2000	1.0470
1.1152	2.0548	2100	1.0410
1.233	2.1526	2200	1.0367
0.9705	2.2505	2300	1.0316
0.8814	2.3483	2400	1.0250
0.967	2.4462	2500	1.0208
1.0735	2.5440	2600	1.0168
1.1725	2.6419	2700	1.0098
1.0661	2.7397	2800	1.0073
1.0255	2.8376	2900	1.0022
1.0509	2.9354	3000	0.9992
1.087	3.0333	3100	0.9942
1.0105	3.1311	3200	0.9897
1.0746	3.2290	3300	0.9860
1.0384	3.3268	3400	0.9833
1.0473	3.4247	3500	0.9791
1.0041	3.5225	3600	0.9744
1.0455	3.6204	3700	0.9713
1.0431	3.7182	3800	0.9675
0.8924	3.8160	3900	0.9645
0.8299	3.9139	4000	0.9611
0.8729	4.0117	4100	0.9583
0.8513	4.1096	4200	0.9556
1.0433	4.2074	4300	0.9512
0.9206	4.3053	4400	0.9491
0.9408	4.4031	4500	0.9461
0.9793	4.5010	4600	0.9433
0.8709	4.5988	4700	0.9402
0.9612	4.6967	4800	0.9370
1.0882	4.7945	4900	0.9346
0.9773	4.8924	5000	0.9331
0.9365	4.9902	5100	0.9302
0.7646	5.0881	5200	0.9276
1.0583	5.1859	5300	0.9246
0.9999	5.2838	5400	0.9223
0.9926	5.3816	5500	0.9207
0.8495	5.4795	5600	0.9194
1.0125	5.5773	5700	0.9163
0.858	5.6751	5800	0.9145
0.9271	5.7730	5900	0.9121
0.9491	5.8708	6000	0.9111
0.998	5.9687	6100	0.9079
1.0376	6.0665	6200	0.9048
0.9302	6.1644	6300	0.9037
0.8533	6.2622	6400	0.9017
0.9285	6.3601	6500	0.8983
0.8977	6.4579	6600	0.8961
0.9745	6.5558	6700	0.8953
0.9405	6.6536	6800	0.8925
0.9929	6.7515	6900	0.8917
0.7314	6.8493	7000	0.8897
0.8764	6.9472	7100	0.8875
0.8443	7.0450	7200	0.8863
0.7612	7.1429	7300	0.8847
1.0069	7.2407	7400	0.8829
1.0436	7.3386	7500	0.8816
0.8622	7.4364	7600	0.8812
0.8886	7.5342	7700	0.8783
0.9365	7.6321	7800	0.8763
0.8307	7.7299	7900	0.8749
0.7874	7.8278	8000	0.8731
0.8606	7.9256	8100	0.8711
0.9094	8.0235	8200	0.8698
0.8108	8.1213	8300	0.8683
1.1008	8.2192	8400	0.8665
0.8168	8.3170	8500	0.8642
0.9125	8.4149	8600	0.8635
0.9218	8.5127	8700	0.8615
0.9688	8.6106	8800	0.8596
0.8679	8.7084	8900	0.8582
0.7928	8.8063	9000	0.8574
0.7947	8.9041	9100	0.8561
0.9055	9.0020	9200	0.8554
1.0261	9.0998	9300	0.8538
0.8588	9.1977	9400	0.8530
0.8789	9.2955	9500	0.8526
0.9111	9.3933	9600	0.8511
0.7969	9.4912	9700	0.8503
0.8683	9.5890	9800	0.8492
0.8884	9.6869	9900	0.8479
0.9843	9.7847	10000	0.8464
0.7575	9.8826	10100	0.8449
0.887	9.9804	10200	0.8441
0.9358	10.0783	10300	0.8428
0.7544	10.1761	10400	0.8422
0.7679	10.2740	10500	0.8411
0.7898	10.3718	10600	0.8406
0.8641	10.4697	10700	0.8397
0.9489	10.5675	10800	0.8385
0.8384	10.6654	10900	0.8380
0.8114	10.7632	11000	0.8372
0.8874	10.8611	11100	0.8361
0.7843	10.9589	11200	0.8349
0.7065	11.0568	11300	0.8343
0.803	11.1546	11400	0.8339
0.827	11.2524	11500	0.8328
0.8149	11.3503	11600	0.8319
0.8248	11.4481	11700	0.8314
0.7901	11.5460	11800	0.8308
0.7693	11.6438	11900	0.8301
0.8768	11.7417	12000	0.8293
0.7997	11.8395	12100	0.8292
0.8565	11.9374	12200	0.8282
0.7635	12.0352	12300	0.8274
0.7206	12.1331	12400	0.8265
0.8557	12.2309	12500	0.8263
0.7447	12.3288	12600	0.8256
0.9202	12.4266	12700	0.8248
0.9656	12.5245	12800	0.8242
0.8597	12.6223	12900	0.8236
0.7596	12.7202	13000	0.8231
0.7913	12.8180	13100	0.8226
0.7493	12.9159	13200	0.8223
0.6831	13.0137	13300	0.8219
0.7939	13.1115	13400	0.8217
0.7227	13.2094	13500	0.8215
0.8662	13.3072	13600	0.8212
0.686	13.4051	13700	0.8210
0.7837	13.5029	13800	0.8206
0.8727	13.6008	13900	0.8202
0.7521	13.6986	14000	0.8199
0.7101	13.7965	14100	0.8196
0.9385	13.8943	14200	0.8193
0.7965	13.9922	14300	0.8191
0.8829	14.0900	14400	0.8188
0.8408	14.1879	14500	0.8186
0.8808	14.2857	14600	0.8185
0.8918	14.3836	14700	0.8184
0.756	14.4814	14800	0.8182
0.8249	14.5793	14900	0.8181
0.721	14.6771	15000	0.8181
0.8631	14.7750	15100	0.8180
0.789	14.8728	15200	0.8180
0.6724	14.9706	15300	0.8180

Framework versions

Transformers 4.52.3
Pytorch 2.6.0+cu124
Datasets 2.14.4
Tokenizers 0.21.1

Hananguyen12/Shakespeare-T5-small

Shakespeare-T5-small

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Hananguyen12/
Shakespeare-T5-small