State-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese. For more details, do check out our Github repo.
Parameters—
Context—
Weights2.7 GB
Licensemit
AccessOpen weights
Monthly Downloads5.3k
Model Card
By VietAI, published under mit, revision 2209a38d735e.
State-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese. For more details, do check out our Github repo.
Read VietAI's full model card
State-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese.
How to use
For more details, do check out our Github repo.
Finetunning Example can be found here.
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("VietAI/vit5-base")
model = AutoModelForSeq2SeqLM.from_pretrained("VietAI/vit5-base")
model.cuda()
Citation
@inproceedings{phan-etal-2022-vit5,
title = "{V}i{T}5: Pretrained Text-to-Text Transformer for {V}ietnamese Language Generation",
author = "Phan, Long and Tran, Hieu and Nguyen, Hieu and Trinh, Trieu H.",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop",
year = "2022",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-srw.18",
pages = "136--142",
}
Configuration
- Architecture
- T5ForConditionalGeneration
- Vocabulary size
- 36,096
- Stored precision
- float32
- Model type
- t5
Identity and Version
- Repository
- VietAI/vit5-base
- Publisher
- VietAI
- Task
- Question answering
- Modality
- Text
- Library
- transformers
- Parameters
- Not stated by the source
- Languages
- vi
- Revision
- 2209a38d735ede63e88f5aa52bcdc11a05a37b85
- First published
- 2022-03-14
- Last updated
- 2022-09-27
Files and Weights
10 files, 2.7 GB in total. The weights are 3 files totalling 2.7 GB in bin, h5, msgpack.
Weights3 files · 2.7 GB
Configuration2 files · 2.8 KB
Tokenizer3 files · 3.2 MB
Documentation1 file · 1.2 KB
Repository1 file · 1.2 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| flax_model.msgpack | Weights | 903.8 MB | 9cce6e8cccab |
| pytorch_model.bin | Weights | 903.9 MB | 6e666460219d |
| tf_model.h5 | Weights | 904.3 MB | 751a7fba06a6 |
| config.json | Configuration | 702 B | — |
| special_tokens_map.json | Configuration | 2.1 KB | — |
| README.md | Documentation | 1.2 KB | — |
| .gitattributes | Repository | 1.2 KB | — |
| spiece.model | Tokenizer | 820.4 KB | 59986b62f9f0 |
| tokenizer.json | Tokenizer | 2.4 MB | — |
| tokenizer_config.json | Tokenizer | 2.2 KB | — |
License and Download
- License
- mit
- Access
- Open weights, no gate
- Download size
- 2.7 GB
Download from VietAI
Released by VietAI through its official repository on Hugging Face. Read the license.
Built From
- Trained on (disclosed) cc100
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 2.7 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About vit5-base
Can I use vit5-base commercially?
Yes. vit5-base is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.
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