| --- |
| base_model: silmi224/finetune-led-35000 |
| tags: |
| - summarization |
| - generated_from_trainer |
| model-index: |
| - name: led-risalah_data_v11 |
| 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. --> |
|
|
| # led-risalah_data_v11 |
|
|
| This model is a fine-tuned version of [silmi224/finetune-led-35000](https://huggingface.co/silmi224/finetune-led-35000) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.6843 |
| - Rouge1 Precision: 0.7035 |
| - Rouge1 Recall: 0.1205 |
| - Rouge1 Fmeasure: 0.2038 |
|
|
| ## 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: 1 |
| - eval_batch_size: 1 |
| - seed: 42 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 4 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 8 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 Precision | Rouge1 Recall | Rouge1 Fmeasure | |
| |:-------------:|:------:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| |
| | 2.6071 | 0.9714 | 17 | 1.8938 | 0.6021 | 0.1074 | 0.1803 | |
| | 1.745 | 2.0 | 35 | 1.7661 | 0.7095 | 0.1174 | 0.1994 | |
| | 1.5717 | 2.9714 | 52 | 1.7251 | 0.6704 | 0.1176 | 0.1968 | |
| | 1.4921 | 4.0 | 70 | 1.6772 | 0.7014 | 0.1175 | 0.1986 | |
| | 1.3932 | 4.9714 | 87 | 1.6745 | 0.7008 | 0.1187 | 0.2011 | |
| | 1.3002 | 6.0 | 105 | 1.6869 | 0.6913 | 0.1196 | 0.2012 | |
| | 1.2784 | 6.9714 | 122 | 1.6857 | 0.7114 | 0.1246 | 0.2097 | |
| | 1.1779 | 7.7714 | 136 | 1.6843 | 0.7035 | 0.1205 | 0.2038 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.41.2 |
| - Pytorch 2.1.2 |
| - Datasets 2.19.2 |
| - Tokenizers 0.19.1 |
| |
