CoolFace
Modelpublic

lst-nectec/HoogBERTa

sourceHugging Facemitupdated 3y agoView on Hugging Face
3likes49downloads
Model Card

HoogBERTa

This repository includes the Thai pretrained language representation (HoogBERTa_base) and can be used for Feature Extraction and Masked Language Modeling Tasks.

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

python
from transformers import AutoTokenizer, AutoModel
from attacut import tokenize
import torch

tokenizer = AutoTokenizer.from_pretrained("lst-nectec/HoogBERTa")
model = AutoModel.from_pretrained("lst-nectec/HoogBERTa")

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

python
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,

python
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

python
with torch.no_grad():
  features = model(token_ids, output_hidden_states = True).hidden_states[-1] # where token_ids is a tensor with type "long".

Huggingface Models

  1. 1.HoogBERTaEncoder
  2. 2.HoogBERTa: Feature Extraction and Mask Language Modeling
  3. 3.HoogBERTaMuliTaskTagger:
  4. 4.HoogBERTa-NER-lst20: Named-entity recognition (NER) based on LST20
  5. 5.HoogBERTa-POS-lst20: Part-of-speech tagging (POS) based on LST20
  6. 6.HoogBERTa-SENTENCE-lst20: Clause Boundary Classification based on LST20

Citation

Please cite as:

bibtex
@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