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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_v7 |
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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_v7 |
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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: 1.1204 |
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- Rouge2 Precision: 0.2764 |
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- Rouge2 Recall: 0.3441 |
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- Rouge2 Fmeasure: 0.2853 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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max_input_length = 1280 |
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max_output_length = 512 |
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led.config.max_length = 512 |
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led.config.min_length = 100 |
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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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| 0.9594 | 0.22 | 10 | 1.3386 | 0.3393 | 0.3048 | 0.2973 | |
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| 1.2189 | 0.44 | 20 | 1.1956 | 0.1935 | 0.314 | 0.2164 | |
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| 1.0542 | 0.67 | 30 | 1.1580 | 0.2383 | 0.4133 | 0.2794 | |
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| 1.5145 | 0.89 | 40 | 1.1204 | 0.2764 | 0.3441 | 0.2853 | |
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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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