license: mit
datasets:
- best2009
- scb_mt_enth_2020
- oscar
- wikipedia
language:
- th
library_name: transformers
HoogBERTa
This repository includes the Thai pretrained language representation (HoogBERTa_base) and the fine-tuned model for multitask sequence labeling.
Documentation
Prerequisite
Since we use subword-nmt BPE encoding, input needs to be pre-tokenize using BEST standard before inputting into HoogBERTa
pip install attacut
Getting Start
To initialize the model from hub, use the following commands
from transformers import AutoTokenizer, AutoModel
from attacut import tokenized
import torch
tokenizer = AutoTokenizer.from_pretrained("new5558/HoogBERTa")
model = AutoModel.from_pretrained("new5558/HoogBERTa")
To annotate POS, NE, and clause boundary, use the following commands
To extract token features, based on the RoBERTa architecture, use the following commands
model.eval()
sentence = "วันที่ 12 มีนาคมนี้ ฉันจะไปเที่ยววัดพระแก้ว ที่กรุงเทพ"
all_sent = []
sentences = sentence.split(" ")
for sent in sentences:
all_sent.append(" ".join(tokenize(sent)).replace("_","[!und:]"))
sentence = " _ ".join(all_sent)
tokenized_text = tokenizer(sentence, return_tensors = 'pt')
token_ids = tokenized_text['input_ids']
with torch.no_grad():
features = model(**tokenized_text, output_hidden_states = True).hidden_states[-1]
For batch processing,
model.eval()
sentenceL = ["วันที่ 12 มีนาคมนี้","ฉันจะไปเที่ยววัดพระแก้ว ที่กรุงเทพ"]
inputList = []
for sentX in sentenceL:
sentences = sentX.split(" ")
all_sent = []
for sent in sentences:
all_sent.append(" ".join(tokenize(sent)).replace("_","[!und:]"))
sentence = " _ ".join(all_sent)
inputList.append(sentence)
tokenized_text = tokenizer(inputList, padding = True, return_tensors = 'pt')
token_ids = tokenized_text['input_ids']
with torch.no_grad():
features = model(**tokenized_text, output_hidden_states = True).hidden_states[-1]
To use HoogBERTa as an embedding layer, use
with torch.no_grad():
features = model(token_ids) # where token_ids is a tensor with type "long".
Conversion Code
If you are interested in how to convert Fairseq and subword-nmt Roberta into Huggingface hub here is my code used to do the conversion and test for parity match: https://www.kaggle.com/norapatbuppodom/hoogberta-conversion
Citation
Please cite as:
@inproceedings{porkaew2021hoogberta,
title = {HoogBERTa: Multi-task Sequence Labeling using Thai Pretrained Language Representation},
author = {Peerachet Porkaew, Prachya Boonkwan and Thepchai Supnithi},
booktitle = {The Joint International Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP 2021)},
year = {2021},
address={Online}
}