Extend vocabulary and Pretrain
We utilized 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
- Vietnamese Wikipedia
- Vietnamese books
- Vietnamese legal documents
- Vietnamese legal text
- English Wikipedia
- Chinese Text
How to use
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
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