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---
base_model: silmi224/finetune-led-35000
tags:
- summarization
- generated_from_trainer
model-index:
- name: led-risalah_data_v15
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_v15
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: 1.6673
- Rouge1 Precision: 0.7043
- Rouge1 Recall: 0.1227
- Rouge1 Fmeasure: 0.2067
## 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: 2e-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
- lr_scheduler_warmup_steps: 300
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 Precision | Rouge1 Recall | Rouge1 Fmeasure |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 3.0403 | 1.0 | 20 | 2.4986 | 0.5024 | 0.0555 | 0.0987 |
| 2.5716 | 2.0 | 40 | 2.1700 | 0.5606 | 0.0817 | 0.1409 |
| 2.2879 | 3.0 | 60 | 2.0072 | 0.5705 | 0.0869 | 0.1492 |
| 2.0807 | 4.0 | 80 | 1.9094 | 0.6048 | 0.0899 | 0.1542 |
| 1.927 | 5.0 | 100 | 1.8184 | 0.5472 | 0.0922 | 0.1561 |
| 1.8368 | 6.0 | 120 | 1.7721 | 0.6079 | 0.1036 | 0.1751 |
| 1.7468 | 7.0 | 140 | 1.7310 | 0.639 | 0.1095 | 0.1842 |
| 1.5913 | 8.0 | 160 | 1.6907 | 0.6637 | 0.1109 | 0.1875 |
| 1.534 | 9.0 | 180 | 1.6843 | 0.6355 | 0.1102 | 0.1851 |
| 1.4835 | 10.0 | 200 | 1.6605 | 0.6596 | 0.1141 | 0.1922 |
| 1.4958 | 11.0 | 220 | 1.6403 | 0.6929 | 0.1162 | 0.1973 |
| 1.4547 | 12.0 | 240 | 1.6347 | 0.6781 | 0.1118 | 0.1892 |
| 1.3069 | 13.0 | 260 | 1.6604 | 0.6626 | 0.1101 | 0.187 |
| 1.2639 | 14.0 | 280 | 1.6712 | 0.697 | 0.1227 | 0.2061 |
| 1.3249 | 15.0 | 300 | 1.6255 | 0.6529 | 0.1135 | 0.1914 |
| 1.185 | 16.0 | 320 | 1.6484 | 0.6806 | 0.1174 | 0.1981 |
| 1.1087 | 17.0 | 340 | 1.6425 | 0.682 | 0.1195 | 0.2008 |
| 1.1125 | 18.0 | 360 | 1.6509 | 0.7122 | 0.1235 | 0.2086 |
| 1.1574 | 19.0 | 380 | 1.6740 | 0.6983 | 0.1214 | 0.2052 |
| 0.9968 | 20.0 | 400 | 1.6673 | 0.7043 | 0.1227 | 0.2067 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
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