use PhoBert(base) https://huggingface.co/vinai/phobert-base to extract embedding vectors (768 dim) for words in sequence(max_len=256, pad=0)
download file from: https://public.vinai.io/PhoBERT_base_transformers.tar.gz or https://huggingface.co/vinai/phobert-base
with folder struct
https://github.com/huggingface/transformers
pip install transformers1
https://github.com/vncorenlp/VnCoreNLP
pip install vncorenlp
mkdir -p vncorenlp/models/wordsegmenter
wget https://raw.githubusercontent.com/vncorenlp/VnCoreNLP/master/VnCoreNLP-1.1.1.jar
wget https://raw.githubusercontent.com/vncorenlp/VnCoreNLP/master/models/wordsegmenter/vi-vocab
wget https://raw.githubusercontent.com/vncorenlp/VnCoreNLP/master/models/wordsegmenter/wordsegmenter.rdr
mv VnCoreNLP-1.1.1.jar vncorenlp/
mv vi-vocab vncorenlp/models/wordsegmenter/
mv wordsegmenter.rdr vncorenlp/models/wordsegmenter/
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train model with tensorflow keras
python train_classifier_keras.py
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train model with transformers(RobertaForSequenceClassification) pytorch
python train_transformers_classifier_pytorch.py
