transformers safetensors pegasus text2text-generation generated_from_trainer base_model:google/pegasus-xsum base_model:finetune:google/pegasus-xsum autotrain_compatible endpoints_compatible region:us
---
base_model: google/pegasus-xsum
tags:
- generated_from_trainer
model-index:
- name: asril-pegasus-xlsum-skripsi
  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. -->

# asril-pegasus-xlsum-skripsi

This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6919

## Model description

this model is spesifically for indonsesian abstractive news article summarization wich has been fine tuning in more than 48k dataset
this model fine-tuned using pegasus model

## Intended uses & limitations

More information needed

## Training and evaluation data

xlsum/indonesian

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 4.5256        | 0.1046 | 1000 | 3.4857          |
| 3.699         | 0.2092 | 2000 | 3.1625          |
| 3.4046        | 0.3138 | 3000 | 2.9968          |
| 3.2456        | 0.4184 | 4000 | 2.8834          |
| 3.126         | 0.5230 | 5000 | 2.8127          |
| 3.055         | 0.6275 | 6000 | 2.7644          |
| 3.005         | 0.7321 | 7000 | 2.7281          |
| 2.9597        | 0.8367 | 8000 | 2.7060          |
| 2.9627        | 0.9413 | 9000 | 2.6919          |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.1.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
