generated_from_trainer

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summarise_v11

This model is a fine-tuned version of allenai/led-base-16384 on the None dataset. It achieves the following results on the evaluation set:

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:

Training results

Training Loss Epoch Step Validation Loss Rouge1 Precision Rouge1 Recall Rouge1 Fmeasure Rouge2 Precision Rouge2 Recall Rouge2 Fmeasure Rougel Precision Rougel Recall Rougel Fmeasure Rougelsum Precision Rougelsum Recall Rougelsum Fmeasure
1.6201 0.45 10 1.4875 0.3203 0.64 0.3932 0.197 0.3839 0.2385 0.1952 0.4051 0.2454 0.1952 0.4051 0.2454
0.9172 0.91 20 1.4404 0.4917 0.5134 0.4699 0.288 0.3095 0.276 0.3371 0.3594 0.3277 0.3371 0.3594 0.3277
1.0923 1.36 30 1.3575 0.519 0.5505 0.4936 0.3114 0.3237 0.2958 0.3569 0.3702 0.3364 0.3569 0.3702 0.3364
1.1287 1.82 40 1.3269 0.4913 0.5997 0.5068 0.3108 0.3964 0.3269 0.3355 0.427 0.3521 0.3355 0.427 0.3521
0.9938 2.27 50 1.3189 0.5339 0.5781 0.4973 0.3555 0.3883 0.3345 0.3914 0.4289 0.3678 0.3914 0.4289 0.3678
0.8659 2.73 60 1.3241 0.525 0.638 0.5165 0.3556 0.4349 0.3535 0.3914 0.4793 0.3886 0.3914 0.4793 0.3886
0.6187 3.18 70 1.3360 0.5875 0.5864 0.5416 0.4005 0.4045 0.3701 0.4485 0.4556 0.414 0.4485 0.4556 0.414
0.3941 3.64 80 1.4176 0.5373 0.6415 0.5328 0.3576 0.446 0.3642 0.3787 0.4586 0.3781 0.3787 0.4586 0.3781
0.4145 4.09 90 1.3936 0.4127 0.6553 0.4568 0.2568 0.4498 0.2988 0.2918 0.4933 0.328 0.2918 0.4933 0.328
0.4203 4.55 100 1.4703 0.6545 0.601 0.5981 0.4789 0.4373 0.438 0.5251 0.4851 0.4818 0.5251 0.4851 0.4818
0.687 5.0 110 1.4304 0.5566 0.6357 0.5637 0.3734 0.4186 0.3748 0.4251 0.4825 0.4286 0.4251 0.4825 0.4286
0.4006 5.45 120 1.5399 0.5994 0.5794 0.5515 0.4215 0.4218 0.398 0.4359 0.4369 0.4084 0.4359 0.4369 0.4084
0.2536 5.91 130 1.5098 0.5074 0.6254 0.4874 0.3369 0.4189 0.3256 0.3802 0.4738 0.3664 0.3802 0.4738 0.3664
0.2218 6.36 140 1.5278 0.5713 0.6059 0.5688 0.3887 0.4233 0.3916 0.4414 0.4795 0.4457 0.4414 0.4795 0.4457
0.2577 6.82 150 1.5469 0.5148 0.5941 0.5175 0.3284 0.3856 0.3335 0.3616 0.4268 0.3681 0.3616 0.4268 0.3681
0.1548 7.27 160 1.5986 0.5983 0.657 0.5862 0.4322 0.4877 0.4287 0.4466 0.5167 0.4482 0.4466 0.5167 0.4482
0.1535 7.73 170 1.5796 0.5609 0.641 0.5616 0.3856 0.4428 0.3892 0.4238 0.4921 0.4263 0.4238 0.4921 0.4263
0.1568 8.18 180 1.6052 0.5669 0.617 0.5679 0.3911 0.4382 0.3969 0.4363 0.4877 0.4417 0.4363 0.4877 0.4417
0.2038 8.64 190 1.6191 0.5466 0.5973 0.5313 0.3543 0.4114 0.3531 0.4061 0.4666 0.404 0.4061 0.4666 0.404
0.1808 9.09 200 1.6165 0.5751 0.5919 0.5587 0.3831 0.4097 0.3817 0.4482 0.4728 0.4405 0.4482 0.4728 0.4405
0.1021 9.55 210 1.6316 0.5316 0.6315 0.535 0.3588 0.4563 0.3697 0.405 0.502 0.4126 0.405 0.502 0.4126
0.1407 10.0 220 1.6322 0.6059 0.6233 0.5895 0.4192 0.4512 0.4176 0.4622 0.4946 0.4566 0.4622 0.4946 0.4566

Framework versions