lst-nectec/HoogBERTa-POS-lst20

HoogBERTa

This repository includes the Thai pretrained language representation (HoogBERTa_base) fine-tuned for Part-of-Speech Tagging (POS) Task.

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 RobertaTokenizerFast, RobertaForTokenClassification
from attacut import tokenize
import torch

tokenizer = RobertaTokenizerFast.from_pretrained("lst-nectec/HoogBERTa-POS-lst20")
model = RobertaForTokenClassification.from_pretrained("lst-nectec/HoogBERTa-POS-lst20")

To do POS Tagging, use the following commands

from transformers import pipeline

nlp = pipeline('token-classification', model=model, tokenizer=tokenizer, aggregation_strategy="none")

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)

print(nlp(sentence))

For batch processing,

from transformers import pipeline

nlp = pipeline('token-classification', model=model, tokenizer=tokenizer, aggregation_strategy="none")

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)

print(nlp(inputList))

Huggingface Models

1. HoogBERTaEncoder

• HoogBERTa: Feature Extraction and Mask Language Modeling

2. HoogBERTaMuliTaskTagger:

• HoogBERTa-NER-lst20: Named-entity recognition (NER) based on LST20
• HoogBERTa-POS-lst20: Part-of-speech tagging (POS) based on LST20
• HoogBERTa-SENTENCE-lst20: Clause Boundary Classification based on LST20

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}
}

Download full-text PDF
Check out the code on Github