---
base_model: silmi224/finetune-led-35000
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: exp2-led-risalah_data_v3
  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_v3

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: 1.9287
- Rouge1: 16.3563
- Rouge2: 6.3361
- Rougel: 10.2361
- Rougelsum: 15.4499

## 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: 150
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 3.3696        | 1.0   | 10   | 2.9032          | 8.9827  | 2.4864 | 6.5741  | 8.374     |
| 3.3479        | 2.0   | 20   | 2.8646          | 9.4368  | 2.6548 | 6.6897  | 8.9073    |
| 3.2858        | 3.0   | 30   | 2.8050          | 8.3204  | 2.4233 | 6.4571  | 7.8334    |
| 3.204         | 4.0   | 40   | 2.7299          | 7.9763  | 2.7995 | 6.1867  | 7.5793    |
| 3.0987        | 5.0   | 50   | 2.6458          | 9.4672  | 2.877  | 7.2221  | 8.8929    |
| 2.9964        | 6.0   | 60   | 2.5576          | 9.3123  | 2.635  | 6.8591  | 8.8136    |
| 2.8831        | 7.0   | 70   | 2.4682          | 9.8347  | 2.8621 | 7.3463  | 9.346     |
| 2.7834        | 8.0   | 80   | 2.3818          | 9.756   | 2.6064 | 7.3736  | 9.0638    |
| 2.6712        | 9.0   | 90   | 2.3005          | 10.6798 | 3.5515 | 7.9318  | 9.5388    |
| 2.5781        | 10.0  | 100  | 2.2261          | 11.4114 | 3.5141 | 8.0732  | 10.6929   |
| 2.4807        | 11.0  | 110  | 2.1623          | 12.9396 | 4.3079 | 9.1668  | 11.7355   |
| 2.403         | 12.0  | 120  | 2.1101          | 13.27   | 4.7477 | 9.0288  | 12.277    |
| 2.3358        | 13.0  | 130  | 2.0644          | 15.1784 | 5.3452 | 10.1318 | 13.8506   |
| 2.2701        | 14.0  | 140  | 2.0249          | 14.1959 | 5.2981 | 10.2128 | 12.8727   |
| 2.2032        | 15.0  | 150  | 1.9925          | 14.4716 | 5.5627 | 9.58    | 13.7089   |
| 2.1608        | 16.0  | 160  | 1.9685          | 14.2815 | 5.9009 | 9.516   | 13.4755   |
| 2.1338        | 17.0  | 170  | 1.9509          | 15.6523 | 6.3449 | 10.2105 | 14.9489   |
| 2.104         | 18.0  | 180  | 1.9383          | 16.3987 | 7.0987 | 10.8261 | 15.8296   |
| 2.0896        | 19.0  | 190  | 1.9308          | 16.0883 | 6.3808 | 10.0722 | 15.17     |
| 2.0758        | 20.0  | 200  | 1.9287          | 16.3563 | 6.3361 | 10.2361 | 15.4499   |


### Framework versions

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