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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_v2 |
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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_v2 |
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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: 2.3235 |
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- Rouge2 Precision: 0.018 |
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- Rouge2 Recall: 0.0916 |
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- Rouge2 Fmeasure: 0.0292 |
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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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| 3.1721 | 0.08 | 10 | 2.7742 | 0.0107 | 0.0671 | 0.0178 | |
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| 3.0802 | 0.16 | 20 | 2.7914 | 0.0111 | 0.0878 | 0.019 | |
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| 3.0795 | 0.24 | 30 | 2.6954 | 0.0094 | 0.076 | 0.0157 | |
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| 2.5806 | 0.32 | 40 | 2.6587 | 0.0028 | 0.0271 | 0.0046 | |
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| 2.6553 | 0.4 | 50 | 2.5958 | 0.0084 | 0.0566 | 0.0143 | |
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| 2.689 | 0.48 | 60 | 2.4857 | 0.0089 | 0.0733 | 0.015 | |
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| 2.6642 | 0.56 | 70 | 2.4205 | 0.0069 | 0.0478 | 0.0116 | |
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| 2.3768 | 0.64 | 80 | 2.3754 | 0.0127 | 0.0795 | 0.0215 | |
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| 2.1949 | 0.72 | 90 | 2.3752 | 0.0155 | 0.1013 | 0.0258 | |
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| 2.3257 | 0.8 | 100 | 2.3509 | 0.0155 | 0.1011 | 0.0261 | |
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| 2.4053 | 0.88 | 110 | 2.3261 | 0.015 | 0.0901 | 0.0246 | |
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| 2.9896 | 0.96 | 120 | 2.3235 | 0.018 | 0.0916 | 0.0292 | |
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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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