Create README.md

First README test

README.md

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+---
+license: mit
+datasets:
+- bigscience-data/roots_vi_binhvq_news_corpus
+- wikipedia
+language:
+- vi
+- en
+- zh
+library_name: transformers
+tags:
+- t5
+- flant5
+- summarization
+- translation
+- question-answering
+---
+## HattoFlanT5-Large
+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()
+```
+## Citation
+Hatto
+IpTech