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---
tags:
- summarization
- generated_from_trainer
model-index:
- name: led-risalah_data_v4
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_v4
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6965
- Rouge1 Precision: 0.7537
- Rouge1 Recall: 0.2044
- Rouge1 Fmeasure: 0.3201
## 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: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 Fmeasure | Rouge1 Precision | Rouge1 Recall |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|:----------------:|:-------------:|
| 2.4649 | 0.91 | 8 | 1.9501 | 0.2231 | 0.5607 | 0.1407 |
| 1.7599 | 1.94 | 17 | 1.7553 | 0.2741 | 0.657 | 0.1746 |
| 1.4655 | 2.97 | 26 | 1.6912 | 0.2786 | 0.6685 | 0.1774 |
| 1.2734 | 4.0 | 35 | 1.7006 | 0.2589 | 0.651 | 0.162 |
| 1.2852 | 4.91 | 43 | 1.6481 | 0.2733 | 0.6657 | 0.1732 |
| 1.1964 | 5.94 | 52 | 1.6380 | 0.263 | 0.6567 | 0.1655 |
| 1.108 | 6.97 | 61 | 1.6441 | 0.2766 | 0.6757 | 0.1746 |
| 1.1023 | 8.91 | 72 | 1.1080 | 0.2842 | 0.6932 | 0.1794 |
| 1.2354 | 9.94 | 81 | 1.1105 | 0.2816 | 0.6858 | 0.1779 |
| 1.1152 | 10.97 | 90 | 1.1317 | 0.2872 | 0.71 | 0.1804 |
| 1.17 | 12.0 | 99 | 1.1206 | 0.2896 | 0.6942 | 0.1837 |
| 1.0691 | 12.91 | 107 | 1.1037 | 0.2941 | 0.7234 | 0.1851 |
| 0.9594 | 13.94 | 116 | 1.1145 | 0.2983 | 0.7299 | 0.1879 |
| 1.0332 | 14.97 | 125 | 1.1295 | 0.2959 | 0.7243 | 0.1863 |
| 0.9519 | 16.0 | 134 | 1.1271 | 0.2916 | 0.7114 | 0.1839 |
| 0.8779 | 16.91 | 142 | 1.1314 | 0.2971 | 0.7192 | 0.1878 |
| 0.944 | 18.91 | 152 | 0.8427 | 0.3212 | 0.7799 | 0.2036 |
| 0.9652 | 19.94 | 161 | 0.8398 | 0.3075 | 0.7396 | 0.1951 |
| 0.9622 | 20.97 | 170 | 0.8421 | 0.3255 | 0.7776 | 0.207 |
| 0.9645 | 22.0 | 179 | 0.8550 | 0.3045 | 0.7283 | 0.1934 |
| 0.8923 | 22.91 | 187 | 0.8556 | 0.3145 | 0.7585 | 0.1992 |
| 0.8635 | 23.94 | 196 | 0.8622 | 0.3086 | 0.7445 | 0.1957 |
| 0.827 | 24.97 | 205 | 0.8648 | 0.3047 | 0.7358 | 0.193 |
| 0.8529 | 26.0 | 214 | 0.8650 | 0.3129 | 0.7586 | 0.1981 |
| 0.7505 | 26.91 | 222 | 0.8719 | 0.3135 | 0.7591 | 0.1985 |
| 0.7491 | 27.94 | 231 | 0.8710 | 0.3078 | 0.7419 | 0.1951 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.15.1
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