metadata
language: vi
tags:
- exbert
license: cc-by-nc-4.0
RoBERTa for Vietnamese and English (envibert)
This RoBERTa version is trained by using 100GB of text (50GB of Vietnamese and 50GB of English) so it is named envibert. The model architecture is custom for production so it only contains 70M parameters.
Usages
from transformers import RobertaModel
from transformers.file_utils import cached_path, hf_bucket_url
from importlib.machinery import SourceFileLoader
import os
cache_dir='./cache'
model_name='nguyenvulebinh/envibert'
def download_tokenizer_files():
resources = ['envibert_tokenizer.py', 'dict.txt', 'sentencepiece.bpe.model']
for item in resources:
if not os.path.exists(os.path.join(cache_dir, item)):
tmp_file = hf_bucket_url(model_name, filename=item)
tmp_file = cached_path(tmp_file,cache_dir=cache_dir)
os.rename(tmp_file, os.path.join(cache_dir, item))
download_tokenizer_files()
tokenizer = SourceFileLoader("envibert.tokenizer", os.path.join(cache_dir,'envibert_tokenizer.py')).load_module().RobertaTokenizer(cache_dir)
model = RobertaModel.from_pretrained(model_name,cache_dir=cache_dir)
# Encode text
text_input = 'Đại học Bách Khoa Hà Nội .'
text_ids = tokenizer(text_input, return_tensors='pt').input_ids
# tensor([[ 0, 705, 131, 8751, 2878, 347, 477, 5, 2]])
# Extract features
text_features = model(text_ids)
text_features['last_hidden_state'].shape
# torch.Size([1, 9, 768])
len(text_features['hidden_states'])
# 7
Citation
@inproceedings{nguyen20d_interspeech,
author={Thai Binh Nguyen and Quang Minh Nguyen and Thi Thu Hien Nguyen and Quoc Truong Do and Chi Mai Luong},
title={{Improving Vietnamese Named Entity Recognition from Speech Using Word Capitalization and Punctuation Recovery Models}},
year=2020,
booktitle={Proc. Interspeech 2020},
pages={4263--4267},
doi={10.21437/Interspeech.2020-1896}
}
Please CITE our repo when it is used to help produce published results or is incorporated into other software.
