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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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- multi_news |
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model-index: |
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- name: summarise_v5 |
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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_v5 |
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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 multi_news dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.3252 |
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- Rouge2 Precision: 0.1458 |
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- Rouge2 Recall: 0.1306 |
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- Rouge2 Fmeasure: 0.1343 |
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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: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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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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| 2.6266 | 0.13 | 10 | 2.4604 | 0.1021 | 0.179 | 0.124 | |
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| 2.4818 | 0.27 | 20 | 2.4122 | 0.1402 | 0.1422 | 0.1345 | |
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| 2.3451 | 0.4 | 30 | 2.3846 | 0.1631 | 0.1177 | 0.1307 | |
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| 2.4462 | 0.53 | 40 | 2.3584 | 0.1671 | 0.1175 | 0.133 | |
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| 2.443 | 0.67 | 50 | 2.3395 | 0.1444 | 0.1359 | 0.1344 | |
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| 2.3822 | 0.8 | 60 | 2.3377 | 0.1517 | 0.1411 | 0.1395 | |
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| 2.4304 | 0.93 | 70 | 2.3252 | 0.1458 | 0.1306 | 0.1343 | |
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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 2.6.2.dev0 |
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- Tokenizers 0.12.1 |
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