---
tags:
- summarization
- generated_from_trainer
model-index:
- name: led-risalah_data_v8
  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_v8

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0169
- Rouge1 Precision: 0.8329
- Rouge1 Recall: 0.135
- Rouge1 Fmeasure: 0.2293

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|:----------------:|:-------------:|
| 1.9061        | 1.0   | 15   | 1.9704          | 0.1528          | 0.5489           | 0.0894        |
| 1.8015        | 2.0   | 30   | 1.7979          | 0.2037          | 0.6934           | 0.1204        |
| 1.6484        | 3.0   | 45   | 1.7690          | 0.2107          | 0.72             | 0.1244        |
| 1.3656        | 4.0   | 60   | 1.7353          | 0.223           | 0.7526           | 0.1321        |
| 1.1833        | 5.0   | 75   | 1.7215          | 0.2172          | 0.7498           | 0.1283        |
| 1.1678        | 6.0   | 90   | 1.7365          | 0.2094          | 0.7063           | 0.1241        |
| 1.1258        | 7.0   | 105  | 1.7643          | 0.2193          | 0.7425           | 0.1299        |
| 1.0591        | 8.0   | 120  | 1.7697          | 0.2184          | 0.7328           | 0.1295        |
| 0.8896        | 9.0   | 135  | 1.7835          | 0.2207          | 0.7391           | 0.1306        |
| 1.0655        | 10.0  | 150  | 1.7985          | 0.2241          | 0.7559           | 0.1325        |
| 0.8386        | 11.0  | 165  | 1.8309          | 0.2217          | 0.7502           | 0.1314        |
| 0.8968        | 12.0  | 180  | 1.8377          | 0.2147          | 0.7179           | 0.1276        |
| 0.7863        | 13.0  | 195  | 1.8737          | 0.2172          | 0.7293           | 0.129         |
| 0.6942        | 14.0  | 210  | 1.8858          | 0.2185          | 0.7489           | 0.1291        |
| 0.6656        | 15.0  | 225  | 1.9181          | 0.2243          | 0.7566           | 0.1328        |
| 0.6672        | 16.0  | 240  | 1.9407          | 0.2224          | 0.7513           | 0.1315        |
| 0.6405        | 17.0  | 255  | 1.9416          | 0.2151          | 0.7369           | 0.1272        |
| 0.7382        | 18.0  | 270  | 1.9533          | 0.2214          | 0.7506           | 0.1311        |
| 0.6445        | 19.0  | 285  | 1.9605          | 0.2136          | 0.7292           | 0.1262        |


### Framework versions

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