| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: summarise |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # summarise |
|
|
| This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.0497 |
| - Rouge2 Precision: 0.3109 |
| - Rouge2 Recall: 0.406 |
| - Rouge2 Fmeasure: 0.3375 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| max_input_length = 3072 |
|
|
| max_output_length = 1000 |
|
|
| led.config.max_length = 1000 |
| |
| led.config.min_length = 100 |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 5e-05 |
| - train_batch_size: 2 |
| - eval_batch_size: 2 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 1 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |
| |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| |
| | 1.7163 | 0.22 | 10 | 1.2307 | 0.1428 | 0.5118 | 0.2089 | |
| | 1.632 | 0.44 | 20 | 1.1337 | 0.36 | 0.3393 | 0.3181 | |
| | 1.0916 | 0.67 | 30 | 1.0738 | 0.2693 | 0.3487 | 0.2731 | |
| | 1.573 | 0.89 | 40 | 1.0497 | 0.3109 | 0.406 | 0.3375 | |
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|
|
| ### Framework versions |
|
|
| - Transformers 4.21.3 |
| - Pytorch 1.12.1+cu113 |
| - Datasets 1.2.1 |
| - Tokenizers 0.12.1 |
|
|
