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License DOI

A Thai word tokenization library using Deep Neural Network.


What's new

  • v0.7.0 Migrate from keras to TensorFlow 2.0
  • v0.6.0 Allow excluding stop words and custom dictionary, updated weight with semi-supervised learning
  • v0.5.2 Better pretrained weight matrix
  • v0.5.1 Faster tokenization by code refactorization
  • examples folder provide starter script for Thai text classification problem
  • DeepcutJS, you can try tokenizing Thai text on web browser here


The Convolutional Neural network is trained from 90 % of NECTEC's BEST corpus (consists of 4 sections, article, news, novel and encyclopedia) and test on the rest 10 %. It is a binary classification model trying to predict whether a character is the beginning of word or not. The results calculated from only 'true' class are as follow

Precision Recall F1
97.8% 98.5% 98.1%


Install using pip for stable release (tensorflow version2.0),

pip install deepcut

For latest development release (recommended),

pip install git+git://

If you want to use tensorflow version 1.x and standalone keras, you will need

pip install deepcut==0.6.1


First, install and run docker on your machine. Then, you can build and run deepcut as follows

docker build -t deepcut:dev . # build docker image
docker run --rm -it deepcut:dev # run docker, -it flag makes it interactive, --rm for clean up the container and remove file system

This will open a shell for us to play with deepcut.


import deepcut

Output will be in list format


Bag-of-word transformation

We implemented a tokenizer which works similar to CountVectorizer from scikit-learn . Here is an example usage:

from deepcut import DeepcutTokenizer
tokenizer = DeepcutTokenizer(ngram_range=(1,1),
                             max_df=1.0, min_df=0.0)
X = tokenizer.fit_tranform(['ฉันบินได้', 'ฉันกินข้าว', 'ฉันอยากบิน']) # 3 x 6 CSR sparse matrix
print(tokenizer.vocabulary_) # {'บิน': 0, 'ได้': 1, 'ฉัน': 2, 'อยาก': 3, 'ข้าว': 4, 'กิน': 5}, column index of sparse matrix

X_test = tokenizer.transform(['ฉันกิน', 'ฉันไม่อยากบิน']) # use built tokenizer vobalurary to transform new text
print(X_test.shape) # 2 x 6 CSR sparse matrix

tokenizer.save_model('tokenizer.pickle') # save the tokenizer to use later

You can load the saved tokenizer to use later

tokenizer = deepcut.load_model('tokenizer.pickle')
X_sample = tokenizer.transform(['ฉันกิน', 'ฉันไม่อยากบิน'])
print(X_sample.shape) # getting the same 2 x 6 CSR sparse matrix as X_test

Custom Dictionary

User can add custom dictionary by adding path to .txt file with one word per line like the following.


The file can be placed as an custom_dict argument in tokenize function e.g.

deepcut.tokenize('ตัดคำได้ดีมาก', custom_dict='/path/to/custom_dict.txt')
deepcut.tokenize('ตัดคำได้ดีมาก', custom_dict=['ดีมาก']) # alternatively, you can provide a list of custom dictionary


Some texts might not be segmented as we would expected (e.g.'โรงเรียน' -> ['โรง', 'เรียน']), this is because of

  • BEST corpus (training data) tokenizes word this way (They use 'Compound words' as a criteria for segmentation)
  • They are unseen/new words -> Ideally, this would be cured by having better corpus but it's not very practical so I am thinking of doing semi-supervised learning to incorporate new examples.

Any suggestion and comment are welcome, please post it in issue section.



If you use deepcut in your project or publication, please cite the library as follows

Rakpong Kittinaradorn, Titipat Achakulvisut, Korakot Chaovavanich, Kittinan Srithaworn,
Pattarawat Chormai, Chanwit Kaewkasi, Tulakan Ruangrong, Krichkorn Oparad.
(2019, September 23). DeepCut: A Thai word tokenization library using Deep Neural Network. Zenodo.

or BibTeX entry:

    author       = {Rakpong Kittinaradorn, Titipat Achakulvisut, Korakot Chaovavanich, Kittinan Srithaworn, Pattarawat Chormai, Chanwit Kaewkasi, Tulakan Ruangrong, Krichkorn Oparad},
    title        = {{DeepCut: A Thai word tokenization library using Deep Neural Network}},
    month        = Sep,
    year         = 2019,
    doi          = {10.5281/zenodo.3457707},
    version      = {1.0},
    publisher    = {Zenodo},
    url          = {}

Partner Organizations

  • True Corporation

We are open for contribution and collaboration.