| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: summarise_v2 |
| 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_v2 |
| |
| 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: 2.3235 |
| - Rouge2 Precision: 0.018 |
| - Rouge2 Recall: 0.0916 |
| - Rouge2 Fmeasure: 0.0292 |
| |
| ## Model description |
| |
| More information needed |
| |
| ## Intended uses & limitations |
| |
| More information needed |
| |
| ## 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 | |
| |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| |
| | 3.1721 | 0.08 | 10 | 2.7742 | 0.0107 | 0.0671 | 0.0178 | |
| | 3.0802 | 0.16 | 20 | 2.7914 | 0.0111 | 0.0878 | 0.019 | |
| | 3.0795 | 0.24 | 30 | 2.6954 | 0.0094 | 0.076 | 0.0157 | |
| | 2.5806 | 0.32 | 40 | 2.6587 | 0.0028 | 0.0271 | 0.0046 | |
| | 2.6553 | 0.4 | 50 | 2.5958 | 0.0084 | 0.0566 | 0.0143 | |
| | 2.689 | 0.48 | 60 | 2.4857 | 0.0089 | 0.0733 | 0.015 | |
| | 2.6642 | 0.56 | 70 | 2.4205 | 0.0069 | 0.0478 | 0.0116 | |
| | 2.3768 | 0.64 | 80 | 2.3754 | 0.0127 | 0.0795 | 0.0215 | |
| | 2.1949 | 0.72 | 90 | 2.3752 | 0.0155 | 0.1013 | 0.0258 | |
| | 2.3257 | 0.8 | 100 | 2.3509 | 0.0155 | 0.1011 | 0.0261 | |
| | 2.4053 | 0.88 | 110 | 2.3261 | 0.015 | 0.0901 | 0.0246 | |
| | 2.9896 | 0.96 | 120 | 2.3235 | 0.018 | 0.0916 | 0.0292 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.21.3 |
| - Pytorch 1.12.1+cu113 |
| - Datasets 1.2.1 |
| - Tokenizers 0.12.1 |
| |
