---
base_model: silmi224/finetune-led-35000
tags:
- summarization
- generated_from_trainer
model-index:
- name: led-risalah_data_v4.1
  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_v4.1

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: 1.6280
- Rouge1 Precision: 0.6744
- Rouge1 Recall: 0.1777
- Rouge1 Fmeasure: 0.2803

## 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: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Rouge1 Precision | Rouge1 Recall | Rouge1 Fmeasure |
|:-------------:|:------:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 2.3322        | 0.9714 | 17   | 1.7433          | 0.6164           | 0.1561        | 0.2485          |
| 1.5523        | 2.0    | 35   | 1.6294          | 0.6379           | 0.1588        | 0.2536          |
| 1.3282        | 2.9714 | 52   | 1.6077          | 0.6252           | 0.1561        | 0.2491          |
| 1.2231        | 4.0    | 70   | 1.5914          | 0.6599           | 0.1704        | 0.2706          |
| 1.1213        | 4.9714 | 87   | 1.6090          | 0.6771           | 0.1707        | 0.272           |
| 1.0491        | 6.0    | 105  | 1.6135          | 0.6656           | 0.17          | 0.2704          |
| 0.9272        | 6.9714 | 122  | 1.6004          | 0.6758           | 0.1755        | 0.2783          |
| 0.8667        | 8.0    | 140  | 1.6217          | 0.7048           | 0.1826        | 0.2896          |
| 0.884         | 8.9714 | 157  | 1.6185          | 0.7143           | 0.1856        | 0.294           |
| 0.8415        | 9.7143 | 170  | 1.6280          | 0.6744           | 0.1777        | 0.2803          |


### Framework versions

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