summarise_v11 / README.md

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	---
	license: apache-2.0
	tags:
	- generated_from_trainer
	model-index:
	- name: summarise_v11
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# summarise_v11

	This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 1.6322
	- Rouge1 Precision: 0.6059
	- Rouge1 Recall: 0.6233
	- Rouge1 Fmeasure: 0.5895
	- Rouge2 Precision: 0.4192
	- Rouge2 Recall: 0.4512
	- Rouge2 Fmeasure: 0.4176
	- Rougel Precision: 0.4622
	- Rougel Recall: 0.4946
	- Rougel Fmeasure: 0.4566
	- Rougelsum Precision: 0.4622
	- Rougelsum Recall: 0.4946
	- Rougelsum Fmeasure: 0.4566

	## 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: 4
	- eval_batch_size: 4
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 10
	- mixed_precision_training: Native AMP

	### 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

	- Transformers 4.21.3
	- Pytorch 1.12.1+cu113
	- Datasets 1.2.1
	- Tokenizers 0.12.1