README.md

---
tags:
- generated_from_keras_callback
model-index:
- name: pegasus_indonesian_base-finetune
  results: []
license: apache-2.0
datasets:
- csebuetnlp/xlsum
- id_liputan6
language:
- id
metrics:
- rouge
pipeline_tag: summarization
library_name: transformers
---

# pegasus_indonesian_base-finetune

Github : [PegasusAnthony](https://github.com/nicholaswilven/PEGASUSAnthony/tree/master)

This model is a fine-tuned version of [pegasus_indonesian_base-pretrain](https://huggingface.co/thonyyy/pegasus_indonesian_base-pretrain) on [Indosum](https://paperswithcode.com/dataset/indosum), [Liputan6](https://paperswithcode.com/dataset/liputan6) and [XLSum](https://huggingface.co/datasets/csebuetnlp/xlsum).

It achieves the following results on the evaluation set:
- Train Loss: 1.6196
- Train Accuracy: 0.1079
- Validation Loss: 1.4097
- Validation Accuracy: 0.1153
- Train Lr: 0.00013661868
- Epoch: 2

## Intended uses & limitations

This model is uncased, can't read special characters except "," and ".", having hard time understanding numbers, and performance only tested on news article text. 

## Performance

| datasets | rouge-1 | rouge-2 | rouge-L |
| ---- | ---- | ---- | ---- |
| Indosum | (TBA) | - | - |
| Liputan6 | 38.27 | 20.22 | 31.26 |
| XLSum | 26.97 | 9.99 | 21.70 |

## Training and evaluation data
Finetune dataset:
1.[Indosum](https://paperswithcode.com/dataset/indosum)
2.[Liputan6](https://paperswithcode.com/dataset/liputan6)
3.[XLSum](https://huggingface.co/datasets/csebuetnlp/xlsum)

## Usage

```python
# Load model and tokenizer
from transformers import TFPegasusForConditionalGeneration, PegasusTokenizerFast
model_name = "thonyyy/pegasus_indonesian_base-finetune"
model = TFPegasusForConditionalGeneration.from_pretrained(model_name)
tokenizer = PegasusTokenizerFast.from_pretrained(model_name)

# Main function to clean text, removes link, bullet point, non ASCII char, parantheses,
# punctuation except "," and ".", numbers with dot (enumerating), extra whitespaces, too short sentences.
import re
import unicodedata
def text_cleaning(input_string):
    lowercase = input_string.lower()
    remove_link = re.sub(r'(https?:\/\/)?([\da-z\.-]+)\.([a-z\.]{2,6})([\/\w\.-]*)', '', lowercase).replace("&","&")
    remove_bullet = "\n".join([T for T in remove_link.split('\n') if '•' not in T and "baca juga:" not in T])
    remove_accented = unicodedata.normalize('NFKD', remove_bullet).encode('ascii', 'ignore').decode('utf-8', 'ignore')
    remove_parentheses = re.sub("([\(\|]).*?([\)\|])", "\g<1>\g<2>", remove_accented)
    remove_punc = re.sub(r"[^\w\d.\s]+",' ', remove_parentheses)
    remove_num_dot = re.sub(r"(?<=\d)\.|\.(?=\d)|(?<=#)\.","", remove_punc)
    remove_extra_whitespace =  re.sub(r'^\s*|\s\s*', ' ', remove_num_dot).strip()
    return ".".join([s for s in remove_extra_whitespace.strip().split('.') if len(s.strip())>10]).replace("_","")

# Article to summarize
sample_article="""
Dana Moneter Internasional (IMF) menilai Indonesia telah menunjukkan pemulihan ekonomi yang baik pasca pandemi melalui kinerja makroekonomi yang kuat, didukung penerapan kebijakan moneter dan fiskal secara berhati-hati. Kebijakan forward looking dan sinergi telah berhasil membawa Indonesia menghadapi tantangan global pada tahun 2022 dengan pertumbuhan yang sehat, tekanan inflasi yang menurun, dan sistem keuangan yang stabil.  Bank Indonesia menyambut baik hasil asesmen IMF atas perekonomian Indonesia dalam laporan Article IV Consultation tahun 2023 yang ​dirilis hari ini (26/6).
Dewan Direktur IMF menyampaikan apresiasi dan catatan positif terhadap berbagai kebijakan yang ditempuh otoritas Indonesia selama tahun 2022. Pertama, keberhasilan otoritas untuk kembali kepada batas maksimal defisit fiskal 3%, lebih cepat dari yang diperkirakan dan komitmen otoritas untuk menerapkan disiplin fiskal. Kedua, penerapan kebijakan moneter yang memadai untuk menjaga stabilitas harga. Ketiga, ketahanan sektor keuangan yang tetap terjaga. Keempat, penerapan UU Cipta Kerja serta UU Pengembangan dan Penguatan Sektor Keuangan, dengan memastikan implementasi yang tepat dan keberlanjutan momentum reformasi untuk mendorong kemudahan berinvestasi, meningkatkan pendalaman pasar keuangan, dan memitigasi dampak scarring dari pandemi. Kelima, strategi diversifikasi Indonesia yang fokus pada upaya hilirisasi dalam rangka meningkatkan nilai tambah ekspor. Keenam, komitmen otoritas untuk mengurangi emisi  gas rumah kaca dan deforestasi.
"""

# Generate summary
x = tokenizer(text_cleaning(t), return_tensors = 'tf')
y = model.generate(**x)
suummary = tokenizer.batch_decode(y, skip_special_tokens=True)
print(summary)

```

## Training procedure
For replication, go to GitHub page

### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: {'name': 'Adafactor', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 0.00013661868, 'beta_2_decay': -0.8, 'epsilon_1': 1e-30, 'epsilon_2': 0.001, 'clip_threshold': 1.0, 'relative_step': True}
- training_precision: float32

### Training results

| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Train Lr      | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-------------:|:-----:|
| 2.3484     | 0.0859         | 1.6304          | 0.1080              | 0.00013661868 | 1     |
| 1.6196     | 0.1079         | 1.4097          | 0.1153              | 0.00013661868 | 2     |


### Framework versions

- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.1
- Tokenizers 0.13.3

### Special Thanks
Research supported with Cloud TPUs from Google’s TPU Research Cloud (TRC)