---
base_model: silmi224/finetune-led-35000
tags:
- summarization
- generated_from_trainer
model-index:
- name: led-risalah_data_v9
  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_v9

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: 1.6418
- Rouge1 Precision: 0.6262
- Rouge1 Recall: 0.3099
- Rouge1 Fmeasure: 0.4143

## 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 |
|:-------------:|:------:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 1.2018        | 0.9714 | 17   | 1.4789          | 0.5782           | 0.2807        | 0.3761          |
| 1.0123        | 2.0    | 35   | 1.4305          | 0.5931           | 0.2892        | 0.3876          |
| 0.8845        | 2.9714 | 52   | 1.4693          | 0.6327           | 0.3088        | 0.4148          |
| 0.705         | 4.0    | 70   | 1.4903          | 0.6263           | 0.3052        | 0.4096          |
| 0.6323        | 4.9714 | 87   | 1.5086          | 0.6167           | 0.3052        | 0.4075          |
| 0.5926        | 6.0    | 105  | 1.5386          | 0.6238           | 0.3031        | 0.4072          |
| 0.5149        | 6.9714 | 122  | 1.5742          | 0.6308           | 0.3035        | 0.4096          |
| 0.4324        | 8.0    | 140  | 1.6112          | 0.6188           | 0.3083        | 0.411           |
| 0.3748        | 8.9714 | 157  | 1.6382          | 0.6262           | 0.3097        | 0.4138          |
| 0.4033        | 9.7143 | 170  | 1.6418          | 0.6262           | 0.3099        | 0.4143          |


### Framework versions

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