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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: summarise_v3 |
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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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# summarise_v3 |
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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 | 2.8968 | 0.0887 | 0.143 | 0.0945 | |
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| 2.6151 | 0.16 | 20 | 2.6183 | 0.1205 | 0.0854 | 0.0907 | |
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| 2.5809 | 0.24 | 30 | 2.4685 | 0.1371 | 0.0748 | 0.0911 | |
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| 2.1297 | 0.32 | 40 | 2.5209 | 0.1481 | 0.092 | 0.1029 | |
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| 2.8083 | 0.4 | 50 | 2.3871 | 0.1785 | 0.1047 | 0.1217 | |
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| 3.0703 | 0.48 | 60 | 2.3674 | 0.1576 | 0.0985 | 0.1103 | |
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| 2.4715 | 0.56 | 70 | 2.3555 | 0.1703 | 0.1036 | 0.1194 | |
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| 2.4538 | 0.64 | 80 | 2.3411 | 0.1619 | 0.0935 | 0.1108 | |
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| 2.3046 | 0.72 | 90 | 2.3105 | 0.152 | 0.0975 | 0.1107 | |
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| 1.7466 | 0.8 | 100 | 2.3416 | 0.1534 | 0.0872 | 0.1038 | |
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| 2.7695 | 0.88 | 110 | 2.3227 | 0.154 | 0.095 | 0.1081 | |
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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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