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summarise_v10

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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_v10
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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_v10
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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: 1.9680
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+ - Rouge1 Precision: 0.4404
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+ - Rouge1 Recall: 0.5874
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+ - Rouge1 Fmeasure: 0.4653
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+ - Rouge2 Precision: 0.2673
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+ - Rouge2 Recall: 0.3871
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+ - Rouge2 Fmeasure: 0.2897
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+ - Rougel Precision: 0.3059
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+ - Rougel Recall: 0.4418
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+ - Rougel Fmeasure: 0.3308
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+ - Rougelsum Precision: 0.3059
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+ - Rougelsum Recall: 0.4418
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+ - Rougelsum Fmeasure: 0.3308
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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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+ | 1.4834 | 0.43 | 10 | 1.7001 | 0.2304 | 0.6761 | 0.3152 | 0.1326 | 0.4034 | 0.1797 | 0.1495 | 0.4624 | 0.2069 | 0.1495 | 0.4624 | 0.2069 |
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+ | 1.5011 | 0.87 | 20 | 1.6051 | 0.4301 | 0.5372 | 0.4087 | 0.2481 | 0.3439 | 0.245 | 0.2878 | 0.3928 | 0.2834 | 0.2878 | 0.3928 | 0.2834 |
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+ | 0.9289 | 1.3 | 30 | 1.5501 | 0.431 | 0.597 | 0.4364 | 0.2653 | 0.393 | 0.2736 | 0.3007 | 0.4233 | 0.3037 | 0.3007 | 0.4233 | 0.3037 |
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+ | 1.0895 | 1.74 | 40 | 1.5969 | 0.4661 | 0.5481 | 0.4486 | 0.2736 | 0.3439 | 0.2689 | 0.3318 | 0.4045 | 0.3221 | 0.3318 | 0.4045 | 0.3221 |
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+ | 0.7785 | 2.17 | 50 | 1.5875 | 0.4527 | 0.5405 | 0.4209 | 0.2942 | 0.3634 | 0.272 | 0.3268 | 0.4047 | 0.3042 | 0.3268 | 0.4047 | 0.3042 |
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+ | 0.635 | 2.61 | 60 | 1.6081 | 0.4142 | 0.5649 | 0.4172 | 0.242 | 0.3659 | 0.2549 | 0.2787 | 0.4156 | 0.2909 | 0.2787 | 0.4156 | 0.2909 |
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+ | 0.514 | 3.04 | 70 | 1.6150 | 0.4431 | 0.5665 | 0.4569 | 0.2656 | 0.3754 | 0.2853 | 0.3252 | 0.441 | 0.3434 | 0.3252 | 0.441 | 0.3434 |
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+ | 0.5617 | 3.48 | 80 | 1.6447 | 0.3956 | 0.6304 | 0.451 | 0.2353 | 0.425 | 0.2776 | 0.2883 | 0.4904 | 0.3332 | 0.2883 | 0.4904 | 0.3332 |
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+ | 0.396 | 3.91 | 90 | 1.7423 | 0.4276 | 0.609 | 0.4506 | 0.2657 | 0.4142 | 0.2858 | 0.3091 | 0.4677 | 0.3316 | 0.3091 | 0.4677 | 0.3316 |
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+ | 0.3427 | 4.35 | 100 | 1.7572 | 0.3877 | 0.5633 | 0.4169 | 0.216 | 0.3635 | 0.2468 | 0.2706 | 0.4314 | 0.3018 | 0.2706 | 0.4314 | 0.3018 |
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+ | 0.3059 | 4.78 | 110 | 1.7705 | 0.4255 | 0.5524 | 0.4429 | 0.2495 | 0.3488 | 0.2671 | 0.3184 | 0.4275 | 0.3358 | 0.3184 | 0.4275 | 0.3358 |
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+ | 0.2083 | 5.22 | 120 | 1.7840 | 0.4533 | 0.5896 | 0.4655 | 0.284 | 0.4142 | 0.308 | 0.3164 | 0.4442 | 0.3376 | 0.3164 | 0.4442 | 0.3376 |
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+ | 0.2591 | 5.65 | 130 | 1.8396 | 0.4391 | 0.5315 | 0.4209 | 0.2768 | 0.3661 | 0.2707 | 0.3194 | 0.4124 | 0.3111 | 0.3194 | 0.4124 | 0.3111 |
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+ | 0.2609 | 6.09 | 140 | 1.8220 | 0.4425 | 0.5712 | 0.4465 | 0.2642 | 0.3738 | 0.2727 | 0.3093 | 0.4349 | 0.3208 | 0.3093 | 0.4349 | 0.3208 |
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+ | 0.1696 | 6.52 | 150 | 1.8916 | 0.475 | 0.5557 | 0.4686 | 0.2959 | 0.3783 | 0.3019 | 0.3409 | 0.4268 | 0.3442 | 0.3409 | 0.4268 | 0.3442 |
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+ | 0.2683 | 6.96 | 160 | 1.8957 | 0.445 | 0.5918 | 0.4748 | 0.285 | 0.4021 | 0.3075 | 0.3249 | 0.4551 | 0.3522 | 0.3249 | 0.4551 | 0.3522 |
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+ | 0.1259 | 7.39 | 170 | 1.9371 | 0.4473 | 0.5368 | 0.4664 | 0.2608 | 0.3355 | 0.282 | 0.3276 | 0.4071 | 0.3492 | 0.3276 | 0.4071 | 0.3492 |
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+ | 0.1919 | 7.83 | 180 | 1.9521 | 0.4026 | 0.5528 | 0.438 | 0.2362 | 0.3427 | 0.2604 | 0.2751 | 0.3957 | 0.3042 | 0.2751 | 0.3957 | 0.3042 |
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+ | 0.1279 | 8.26 | 190 | 1.9398 | 0.413 | 0.6053 | 0.4575 | 0.2511 | 0.403 | 0.2881 | 0.2662 | 0.4195 | 0.3027 | 0.2662 | 0.4195 | 0.3027 |
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+ | 0.1176 | 8.7 | 200 | 1.9556 | 0.4363 | 0.565 | 0.4492 | 0.2591 | 0.3727 | 0.2806 | 0.3107 | 0.428 | 0.3289 | 0.3107 | 0.428 | 0.3289 |
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+ | 0.1299 | 9.13 | 210 | 1.9642 | 0.4385 | 0.5728 | 0.4587 | 0.2687 | 0.3744 | 0.2888 | 0.3212 | 0.436 | 0.3404 | 0.3212 | 0.436 | 0.3404 |
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+ | 0.1303 | 9.57 | 220 | 1.9649 | 0.43 | 0.5648 | 0.439 | 0.2605 | 0.3624 | 0.2691 | 0.2958 | 0.4135 | 0.3067 | 0.2958 | 0.4135 | 0.3067 |
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+ | 0.1129 | 10.0 | 230 | 1.9680 | 0.4404 | 0.5874 | 0.4653 | 0.2673 | 0.3871 | 0.2897 | 0.3059 | 0.4418 | 0.3308 | 0.3059 | 0.4418 | 0.3308 |
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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