---
tags:
- summarization
- generated_from_trainer
model-index:
- name: led-risalah_data_v2
  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_v2

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7850
- Rouge1 Precision: 0.816
- Rouge1 Recall: 0.2149
- Rouge1 Fmeasure: 0.3393

## 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: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Rouge1 Fmeasure | Rouge1 Precision | Rouge1 Recall |
|:-------------:|:-------:|:----:|:---------------:|:---------------:|:----------------:|:-------------:|
| 2.4163        | 0.9143  | 8    | 1.9482          | 0.2001          | 0.4982           | 0.1254        |
| 1.6578        | 1.9429  | 17   | 1.8076          | 0.2489          | 0.6295           | 0.1554        |
| 1.656         | 2.9143  | 24   | 1.4664          | 0.2459          | 0.6118           | 0.154         |
| 1.5142        | 3.9429  | 33   | 1.4191          | 0.2546          | 0.646            | 0.159         |
| 1.4169        | 4.9714  | 42   | 1.4162          | 0.27            | 0.6675           | 0.1698        |
| 1.4123        | 6.9143  | 56   | 1.3197          | 0.2807          | 0.7054           | 0.1755        |
| 1.3398        | 7.9429  | 65   | 1.3156          | 0.2797          | 0.6912           | 0.1759        |
| 1.146         | 8.9714  | 74   | 1.3247          | 0.2925          | 0.728            | 0.1834        |
| 1.1481        | 10.0    | 83   | 1.3366          | 0.2739          | 0.6799           | 0.1718        |
| 1.2033        | 10.9143 | 91   | 1.3387          | 0.2789          | 0.69             | 0.1752        |
| 1.0855        | 11.9429 | 100  | 1.3375          | 0.2888          | 0.7146           | 0.1814        |
| 0.999         | 12.9714 | 109  | 1.3589          | 0.2922          | 0.7265           | 0.1831        |
| 1.0034        | 14.0    | 118  | 1.3601          | 0.2872          | 0.7157           | 0.1801        |
| 0.9831        | 14.9143 | 126  | 1.3762          | 0.2851          | 0.7024           | 0.1792        |
| 0.9347        | 15.9429 | 135  | 1.3743          | 0.2769          | 0.6841           | 0.174         |
| 0.9018        | 16.9714 | 144  | 1.3820          | 0.2862          | 0.7139           | 0.1797        |
| 0.8939        | 18.0    | 153  | 1.3841          | 0.2879          | 0.7134           | 0.1806        |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1