README.md

metadata

license: mit
datasets:
  - scb_mt_enth_2020
  - oscar
  - best2009
  - wikipedia
language:
  - th
library_name: fairseq

HoogBERTa

This repository includes the Thai pretrained language representation (HoogBERTa_base) and the fine-tuned model for multitask sequence labeling.

Documentation

To initialize the model from hub, use the following commands

from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("new5558/HoogBERTa")
model = AutoModel.from_pretrained("new5558/HoogBERTa")

To annotate POS, NE and cluase boundary, use the following commands

To extract token features, based on the RoBERTa architecture, use the following commands

with torch.no_grad():
    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)
    token_ids = tokenizer(sentence, return_tensors = 'pt')['input_ids']
    features = model(token_ids)

For batch processing,

with torch.no_grad():
    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)
    token_ids = tokenizer(inputList, padding = True, return_tensors = 'pt').input_ids
    features = model(token_ids)

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".

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}
}

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