flan-t5-base-xlsum

This model is a fine-tuned version of google/flan-t5-base on the xlsum dataset. It achieves the following results on the evaluation set:

• Loss: 0.3988

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: 5e-05
• train_batch_size: 6
• eval_batch_size: 12
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 200
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss
17.0416	0.09	200	0.4680
0.495	0.18	400	0.4080
0.4721	0.28	600	0.4051
0.4677	0.37	800	0.4048
0.4691	0.46	1000	0.4035
0.4667	0.55	1200	0.4025
0.464	0.65	1400	0.4015
0.4575	0.74	1600	0.4004
0.4598	0.83	1800	0.4003
0.4602	0.92	2000	0.4004
0.4556	1.02	2200	0.3992
0.4335	1.11	2400	0.3992
0.4347	1.2	2600	0.3992
0.4421	1.29	2800	0.3999
0.4318	1.39	3000	0.3988
0.4425	1.48	3200	0.3981
0.4428	1.57	3400	0.3988
0.4345	1.66	3600	0.3980
0.4266	1.76	3800	0.3979
0.4245	1.85	4000	0.3982
0.4215	1.94	4200	0.3967
0.4285	2.03	4400	0.3977
0.4082	2.13	4600	0.3981
0.4049	2.22	4800	0.3980
0.4139	2.31	5000	0.3975
0.4008	2.4	5200	0.3983
0.4073	2.5	5400	0.3980
0.4214	2.59	5600	0.3979
0.411	2.68	5800	0.3983
0.4173	2.77	6000	0.3971
0.4098	2.87	6200	0.3970
0.4267	2.96	6400	0.3968
0.3958	3.05	6600	0.3976
0.3959	3.14	6800	0.3984
0.4049	3.24	7000	0.3978
0.3993	3.33	7200	0.3980
0.3971	3.42	7400	0.3983
0.4032	3.51	7600	0.3987
0.391	3.61	7800	0.3987
0.3988	3.7	8000	0.3990
0.3912	3.79	8200	0.3984
0.3958	3.88	8400	0.3982
0.396	3.98	8600	0.3979
0.3926	4.07	8800	0.3988
0.3913	4.16	9000	0.3988
0.3915	4.25	9200	0.3985
0.3885	4.35	9400	0.3988
0.3824	4.44	9600	0.3991
0.3884	4.53	9800	0.3989
0.3816	4.62	10000	0.3989
0.4028	4.72	10200	0.3986
0.3881	4.81	10400	0.3988
0.3809	4.9	10600	0.3988
0.3873	4.99	10800	0.3988

Framework versions

• Transformers 4.31.0
• Pytorch 2.0.1+cu117
• Datasets 2.13.1
• Tokenizers 0.13.3