---
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: 1.6785
- Rouge1 Precision: 0.6665
- Rouge1 Recall: 0.1816
- Rouge1 Fmeasure: 0.284

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|:----------------:|:-------------:|
| 2.4597        | 0.91  | 8    | 1.8034          | 0.1951          | 0.4699           | 0.1246        |
| 1.7706        | 1.94  | 17   | 1.6403          | 0.2451          | 0.6043           | 0.1554        |
| 1.5072        | 2.97  | 26   | 1.5947          | 0.2628          | 0.6236           | 0.1676        |
| 1.4018        | 4.0   | 35   | 1.5688          | 0.2789          | 0.656            | 0.1782        |
| 1.2761        | 4.91  | 43   | 1.5454          | 0.2723          | 0.6434           | 0.1736        |
| 1.1779        | 5.94  | 52   | 1.5636          | 0.2889          | 0.6794           | 0.1843        |
| 1.1235        | 6.97  | 61   | 1.5430          | 0.2965          | 0.6913           | 0.1902        |
| 1.0529        | 8.0   | 70   | 1.5639          | 0.2829          | 0.6705           | 0.1805        |
| 0.9883        | 8.91  | 78   | 1.5740          | 0.2817          | 0.6757           | 0.1798        |
| 0.9274        | 9.94  | 87   | 1.5793          | 0.2771          | 0.6623           | 0.1764        |
| 0.925         | 10.97 | 96   | 1.6072          | 0.2821          | 0.665            | 0.18          |
| 0.858         | 12.0  | 105  | 1.6129          | 0.284           | 0.6625           | 0.1817        |
| 0.8182        | 12.91 | 113  | 1.6396          | 0.2765          | 0.6567           | 0.1761        |
| 0.7974        | 13.94 | 122  | 1.6445          | 0.2759          | 0.659            | 0.1759        |
| 0.7524        | 14.97 | 131  | 1.6585          | 0.2763          | 0.6585           | 0.1759        |
| 0.7743        | 16.0  | 140  | 1.6779          | 0.2788          | 0.6594           | 0.1779        |
| 0.7486        | 16.91 | 148  | 1.6742          | 0.2851          | 0.6666           | 0.1819        |
| 0.676         | 17.94 | 157  | 1.6790          | 0.2859          | 0.6707           | 0.1827        |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.15.1