| --- |
| base_model: silmi224/finetune-led-35000 |
| tags: |
| - summarization |
| - generated_from_trainer |
| model-index: |
| - name: led-risalah_data_v9 |
| 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_v9 |
|
|
| 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: 1.6418 |
| - Rouge1 Precision: 0.6262 |
| - Rouge1 Recall: 0.3099 |
| - Rouge1 Fmeasure: 0.4143 |
|
|
| ## 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: 10 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 Precision | Rouge1 Recall | Rouge1 Fmeasure | |
| |:-------------:|:------:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| |
| | 1.2018 | 0.9714 | 17 | 1.4789 | 0.5782 | 0.2807 | 0.3761 | |
| | 1.0123 | 2.0 | 35 | 1.4305 | 0.5931 | 0.2892 | 0.3876 | |
| | 0.8845 | 2.9714 | 52 | 1.4693 | 0.6327 | 0.3088 | 0.4148 | |
| | 0.705 | 4.0 | 70 | 1.4903 | 0.6263 | 0.3052 | 0.4096 | |
| | 0.6323 | 4.9714 | 87 | 1.5086 | 0.6167 | 0.3052 | 0.4075 | |
| | 0.5926 | 6.0 | 105 | 1.5386 | 0.6238 | 0.3031 | 0.4072 | |
| | 0.5149 | 6.9714 | 122 | 1.5742 | 0.6308 | 0.3035 | 0.4096 | |
| | 0.4324 | 8.0 | 140 | 1.6112 | 0.6188 | 0.3083 | 0.411 | |
| | 0.3748 | 8.9714 | 157 | 1.6382 | 0.6262 | 0.3097 | 0.4138 | |
| | 0.4033 | 9.7143 | 170 | 1.6418 | 0.6262 | 0.3099 | 0.4143 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.41.2 |
| - Pytorch 2.1.2 |
| - Datasets 2.19.2 |
| - Tokenizers 0.19.1 |
| |
