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# assamim/mt5-pukulenam-summarization

This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggingface.co/csebuetnlp/mT5_multilingual_XLSum) on an [csebuetnlp/xlsum](https://huggingface.co/datasets/csebuetnlp/xlsum) dataset

## Using this model in `transformers` (tested on 4.19.2)

```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import re
news = """
Anggota Unit Perlindungan Rakyat Kurdi di kota Rabia, pada perbatasan Irak-Suriah. Pasukan Kurdi Irak dilaporkan sudah menguasai kembali kota Rabia meskipun banyak korban jatuh. Pejabat senior Kurdi Irak mengatakan pasukan Kurdi Peshmerga mencatat kemajuan lewat serangan dini hari di Rabia. Sementara itu, milisi ISIS berusaha memukul mundur pasukan Kurdi Suriah di bagian lain perbatasan. Hal ini terjadi saat koalisi pimpinan Amerika terus melanjutkan serangan udara terhadap sasaran ISIS di Suriah dan Irak. Hari Selasa (30 September) dilaporkan juga terjadi serangkaian serangan bom di ibu kota Irak, Baghdad dan kota suci Syiah, Karbala. Dalam perkembangan terpisah, sejumlah tank Turki berada di bukit di sepanjang perbatasan dekat kota Kobane, Suriah setelah sejumlah bom mengenai wilayah Turki saat terjadi bentrokan dengan milisi ISIS dan pejuang Kurdi. Pemerintah Turki diperkirakan akan menyampaikan mosi ke parlemen, agar menyetujui aksi militer terhadap ISIS di Irak dan Suriah.
"""
tokenizer = AutoTokenizer.from_pretrained("assamim/mt5-pukulenam-summarization")
model = AutoModelForSeq2SeqLM.from_pretrained("assamim/mt5-pukulenam-summarization", from_tf=True)
WHITESPACE_HANDLER = lambda k: re.sub('\s+', ' ', re.sub('\n+', ' ', k.strip()))
input_ids = tokenizer.encode(WHITESPACE_HANDLER(news1), return_tensors='pt')
summary_ids = model.generate(input_ids,
            min_length=20,
            max_length=200,
            num_beams=7,
            repetition_penalty=2.5,
            length_penalty=1.0,
            early_stopping=True,
            no_repeat_ngram_size=2,
            use_cache=True,
            do_sample = True,
            temperature = 0.8,
            top_k = 50,
            top_p = 0.95)
summary_text = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print(summary_text)
```

### Framework versions

- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1

{"tags": ["generated_from_keras_callback", "Summarization", "mT5"], "datasets": ["csebuetnlp/xlsum"], "model-index": [{"name": "assamim/mt5-pukulenam-summarization", "results": []}]}
transformers tf mt5 text2text-generation generated_from_keras_callback Summarization mT5 dataset:csebuetnlp/xlsum autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #Summarization #mT5 #dataset-csebuetnlp/xlsum #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 




# assamim/mt5-pukulenam-summarization

This model is a fine-tuned version of csebuetnlp/mT5_multilingual_XLSum on an csebuetnlp/xlsum dataset

## Using this model in 'transformers' (tested on 4.19.2)



### Framework versions

- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1

# assamim/mt5-pukulenam-summarization

This model is a fine-tuned version of csebuetnlp/mT5_multilingual_XLSum on an csebuetnlp/xlsum dataset ## Using this model in 'transformers' (tested on 4.19.2) ### Framework versions

- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
TAGS
#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #Summarization #mT5 #dataset-csebuetnlp/xlsum #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # assamim/mt5-pukulenam-summarization

This model is a fine-tuned version of csebuetnlp/mT5_multilingual_XLSum on an csebuetnlp/xlsum dataset ## Using this model in 'transformers' (tested on 4.19.2) ### Framework versions

- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
passage: TAGS
#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #Summarization #mT5 #dataset-csebuetnlp/xlsum #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# assamim/mt5-pukulenam-summarization

This model is a fine-tuned version of csebuetnlp/mT5_multilingual_XLSum on an csebuetnlp/xlsum dataset## Using this model in 'transformers' (tested on 4.19.2)### Framework versions

- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1