---
base_model: silmi224/finetune-led-35000
tags:
- summarization
- generated_from_trainer
model-index:
- name: led-risalah_data_v14
  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_v14

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: 1.7199
- Rouge1 Precision: 0.6769
- Rouge1 Recall: 0.1724
- Rouge1 Fmeasure: 0.2744

## 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: 2e-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 |
|:-------------:|:------:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 1.6706        | 0.9714 | 17   | 1.8400          | 0.6261           | 0.1547        | 0.2478          |
| 1.5177        | 2.0    | 35   | 1.7573          | 0.6586           | 0.1669        | 0.266           |
| 1.4016        | 2.9714 | 52   | 1.7266          | 0.6597           | 0.1689        | 0.2682          |
| 1.3182        | 4.0    | 70   | 1.7403          | 0.6564           | 0.1667        | 0.2653          |
| 1.217         | 4.9714 | 87   | 1.7272          | 0.657            | 0.1663        | 0.265           |
| 1.1559        | 6.0    | 105  | 1.7288          | 0.6493           | 0.1698        | 0.2687          |
| 1.1675        | 6.9714 | 122  | 1.7114          | 0.6727           | 0.1705        | 0.2717          |
| 1.1193        | 8.0    | 140  | 1.7118          | 0.6764           | 0.1734        | 0.2758          |
| 1.1101        | 8.9714 | 157  | 1.7232          | 0.6705           | 0.1726        | 0.2739          |
| 1.147         | 9.7143 | 170  | 1.7199          | 0.6769           | 0.1724        | 0.2744          |


### Framework versions

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