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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_v8 |
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results: [] |
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--- |
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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 SGH news articles and summaries dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8163 |
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- Rouge2 Precision: 0.3628 |
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- Rouge2 Recall: 0.3589 |
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- Rouge2 Fmeasure: 0.3316 |
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## Model description |
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This model was created to generate summaries of news articles. |
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## Intended uses & limitations |
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The model takes up to maximum article length of 768 tokens and generates a summary of maximum length of 512 tokens, and minimum length of 100 tokens. |
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## Training and evaluation data |
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This model was trained on 100+ articles and summaries from SGH. |
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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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| 1.5952 | 0.23 | 10 | 1.0414 | 0.2823 | 0.3908 | 0.3013 | |
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| 1.8116 | 0.47 | 20 | 0.9171 | 0.3728 | 0.273 | 0.3056 | |
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| 1.6289 | 0.7 | 30 | 0.8553 | 0.3284 | 0.2892 | 0.291 | |
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| 1.5074 | 0.93 | 40 | 0.8163 | 0.3628 | 0.3589 | 0.3316 | |
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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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