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summarise_v11

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+ ---
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+ license: apache-2.0
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: summarise_v11
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # summarise_v11
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+
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+ This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.6322
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+ - Rouge1 Precision: 0.6059
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+ - Rouge1 Recall: 0.6233
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+ - Rouge1 Fmeasure: 0.5895
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+ - Rouge2 Precision: 0.4192
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+ - Rouge2 Recall: 0.4512
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+ - Rouge2 Fmeasure: 0.4176
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+ - Rougel Precision: 0.4622
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+ - Rougel Recall: 0.4946
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+ - Rougel Fmeasure: 0.4566
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+ - Rougelsum Precision: 0.4622
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+ - Rougelsum Recall: 0.4946
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+ - Rougelsum Fmeasure: 0.4566
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 4
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+ - eval_batch_size: 4
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 10
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | 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 |
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+ |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:-------------------:|:----------------:|:------------------:|
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.21.3
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+ - Pytorch 1.12.1+cu113
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+ - Datasets 1.2.1
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+ - Tokenizers 0.12.1