---
base_model: silmi224/finetune-led-35000
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: exp2-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. -->

# exp2-led-risalah_data_v4

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: 1.8431
- Rouge1: 16.5193
- Rouge2: 8.3503
- Rougel: 11.7271
- Rougelsum: 15.6162

## 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: 1e-06
- 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
- lr_scheduler_warmup_steps: 300
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 3.3717        | 1.0   | 10   | 2.9094          | 8.8016  | 2.3126 | 6.2771  | 8.3716    |
| 3.3649        | 2.0   | 20   | 2.8898          | 9.2296  | 2.5864 | 6.5169  | 8.8408    |
| 3.3317        | 3.0   | 30   | 2.8578          | 9.4144  | 2.7476 | 6.7319  | 8.9607    |
| 3.2876        | 4.0   | 40   | 2.8156          | 9.2048  | 2.6478 | 6.8107  | 8.8212    |
| 3.2244        | 5.0   | 50   | 2.7651          | 7.4966  | 2.3382 | 5.9094  | 6.9392    |
| 3.1638        | 6.0   | 60   | 2.7088          | 8.8105  | 2.6633 | 6.809   | 8.3272    |
| 3.087         | 7.0   | 70   | 2.6486          | 9.3756  | 2.6957 | 7.2067  | 9.0197    |
| 3.0201        | 8.0   | 80   | 2.5859          | 9.5975  | 2.7885 | 6.9418  | 9.0329    |
| 2.9335        | 9.0   | 90   | 2.5224          | 9.5107  | 2.374  | 6.8494  | 8.9865    |
| 2.8603        | 10.0  | 100  | 2.4585          | 9.8073  | 2.8793 | 7.4445  | 9.4102    |
| 2.7774        | 11.0  | 110  | 2.3954          | 10.604  | 2.8025 | 7.8035  | 10.1927   |
| 2.7011        | 12.0  | 120  | 2.3347          | 10.3728 | 3.4421 | 7.8112  | 9.5918    |
| 2.634         | 13.0  | 130  | 2.2783          | 11.0596 | 3.3087 | 7.9686  | 10.047    |
| 2.5608        | 14.0  | 140  | 2.2253          | 12.4204 | 4.4276 | 8.5552  | 11.4364   |
| 2.4866        | 15.0  | 150  | 2.1782          | 12.8046 | 4.4267 | 8.8782  | 12.2253   |
| 2.4349        | 16.0  | 160  | 2.1369          | 13.0668 | 4.3763 | 8.7619  | 12.104    |
| 2.3851        | 17.0  | 170  | 2.1012          | 13.7679 | 4.6022 | 9.1874  | 12.7284   |
| 2.3302        | 18.0  | 180  | 2.0691          | 13.2512 | 4.6911 | 9.3187  | 11.8059   |
| 2.2836        | 19.0  | 190  | 2.0403          | 14.3491 | 5.7839 | 9.8346  | 13.3638   |
| 2.236         | 20.0  | 200  | 2.0150          | 13.9778 | 4.9493 | 9.5799  | 12.6063   |
| 2.1965        | 21.0  | 210  | 1.9910          | 14.0795 | 5.1926 | 9.3653  | 13.3801   |
| 2.1586        | 22.0  | 220  | 1.9704          | 14.1261 | 5.9801 | 9.7882  | 13.503    |
| 2.1325        | 23.0  | 230  | 1.9513          | 14.3575 | 6.0074 | 9.6053  | 13.672    |
| 2.099         | 24.0  | 240  | 1.9332          | 15.6132 | 6.3777 | 10.3533 | 14.9225   |
| 2.0703        | 25.0  | 250  | 1.9141          | 16.145  | 6.8437 | 10.6729 | 15.0299   |
| 2.0438        | 26.0  | 260  | 1.8984          | 15.3881 | 6.5977 | 10.048  | 14.7873   |
| 2.0187        | 27.0  | 270  | 1.8846          | 14.1595 | 6.3778 | 9.4685  | 13.3986   |
| 1.9954        | 28.0  | 280  | 1.8693          | 14.2631 | 6.3966 | 10.4774 | 13.4271   |
| 1.9723        | 29.0  | 290  | 1.8576          | 15.878  | 6.6511 | 10.8733 | 14.6417   |
| 1.9465        | 30.0  | 300  | 1.8431          | 16.5193 | 8.3503 | 11.7271 | 15.6162   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1