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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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datasets: |
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- scientific_papers |
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model-index: |
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- name: longformer_summarise |
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results: [] |
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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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# longformer_summarise |
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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 scientific_papers dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.3003 |
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- Rouge2 Precision: 0.1654 |
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- Rouge2 Recall: 0.0966 |
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- Rouge2 Fmeasure: 0.1118 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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: 2 |
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- eval_batch_size: 2 |
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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: 1 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |
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|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| |
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| 2.909 | 0.08 | 10 | 2.8969 | 0.09 | 0.1439 | 0.0953 | |
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| 2.615 | 0.16 | 20 | 2.6182 | 0.1232 | 0.0865 | 0.0924 | |
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| 2.581 | 0.24 | 30 | 2.4687 | 0.1357 | 0.0733 | 0.09 | |
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| 2.1294 | 0.32 | 40 | 2.5215 | 0.1495 | 0.0932 | 0.1044 | |
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| 2.8083 | 0.4 | 50 | 2.3870 | 0.1794 | 0.1054 | 0.1224 | |
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| 3.0704 | 0.48 | 60 | 2.3676 | 0.1572 | 0.0989 | 0.1108 | |
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| 2.4716 | 0.56 | 70 | 2.3554 | 0.1707 | 0.1039 | 0.1198 | |
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| 2.454 | 0.64 | 80 | 2.3411 | 0.1619 | 0.0943 | 0.1115 | |
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| 2.3046 | 0.72 | 90 | 2.3105 | 0.1547 | 0.0965 | 0.1116 | |
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| 1.7467 | 0.8 | 100 | 2.3417 | 0.1551 | 0.0877 | 0.1046 | |
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| 2.7696 | 0.88 | 110 | 2.3226 | 0.1543 | 0.0954 | 0.1085 | |
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| 2.4999 | 0.96 | 120 | 2.3003 | 0.1654 | 0.0966 | 0.1118 | |
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### Framework versions |
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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 |
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