---
license: apache-2.0
datasets:
- bigscience-data/roots_vi_binhvq_news_corpus
- wikipedia
language:
- vi
- en
- zh
library_name: transformers
tags:
- t5
- flant5
- summarization
- translation
- question-answering
pipeline_tag: fill-mask
---
## Extend vocabulary and Pretrain
We utilized [SentencePiece](https://github.com/google/sentencepiece) to retrain a tokenizer for Vietnamese, English, and Chinese. This newly trained tokenizer's vocabulary was then combined with Flan-T5's original vocabulary, eliminating any duplicate tokens. The resulting merged vocabulary consists of 106611 tokens.

For a single-epoch continual pretraining, also referred to as incremental pretraining, we employed the Flan-T5-Large model. This pretraining was conducted on a diverse dataset exceeding 100 GB, incorporating the following sources:
- [NewsCorpus](https://github.com/binhvq/news-corpus)
- Vietnamese Wikipedia
- Vietnamese books
- Vietnamese legal documents
- Vietnamese legal text
- English Wikipedia
- Chinese Text
  
## How to use
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Hatto/HattoFlanT5-Large")  
model = AutoModelForSeq2SeqLM.from_pretrained("Hatto/HattoFlanT5-Large")
model.cuda()
```

## Finetune and Benchmark

- Wikilingua
- Vietnews
- Pho_NER
- .....

## Citation
- Hatto
- Ipcoms