ELECTRA is a new method for self-supervised language representation learning. This repository contains the pre-trained Electra small model (tensorflow 2.1.0) trained in a large Vietnamese corpus (~50GB of text).

According to the author's description:

Inspired by generative adversarial networks (GANs), ELECTRA trains the model to distinguish between “real” and “fake” input data. Instead of corrupting the input by replacing tokens with “[MASK]” as in BERT, our approach corrupts the input by replacing some input tokens with incorrect, but somewhat plausible, fakes. For example, in the below figure, the word “cooked” could be replaced with “ate”. While this makes a bit of sense, it doesn’t fit as well with the entire context. The pre-training task requires the model (i.e., the discriminator) to then determine which tokens from the original input have been replaced or kept the same.

All corpus was tokenize using coccoc-tokenizer. To using this trained model correctly, let install coccoc-tokenizer lib first.

# Create new env
conda create -n electra-tf python=3.7
conda activate electra-tf
pip install -r requirements.txt

# Install coc coc tokenizer
git clone https://github.com/coccoc/coccoc-tokenizer.git
cd coccoc-tokenizer
mkdir build && cd build
cmake -DBUILD_PYTHON=1 ..
make install
cd ../python
python setup.py install

Let follow this tutorial to use the trained model. You can also play around in this colab notebook.

Extract features from electra:

from electra_model_tf2 import TFElectraDis
from tokenizers.implementations import SentencePieceBPETokenizer
import tensorflow as tf

# Create tokenizer
tokenizer = SentencePieceBPETokenizer(
    "./vocab/vocab.json",
    "./vocab/merges.txt",
)

vi_electra = TFElectraDis.from_pretrained('./model_pretrained/dis/')

tokenizer.add_special_tokens(["[PAD]", "[UNK]", "[CLS]", "[SEP]", "[MASK]"])
text = "Sinh_viên trường Đại_học Bách_Khoa Hà_Nội"
text_encode = tokenizer.encode(text)
indices = [tokenizer.token_to_id("[CLS]")] + text_encode.ids + [tokenizer.token_to_id("[SEP]")]
assert indices == [64002, 15429, 1782, 5111, 29625, 2052, 64003]

features = vi_electra(tf.constant([indices]))
assert features[0].shape == (7,)  # discriminator detect replaced word
assert len(features[1]) == 13  # discriminator features (12 hidden layers + 1 output layer)
assert features[1][-1].shape == (1, 7, 256)  # 1 sample, 7 words, 256 features dimensions

Please follow the root repository for training model.

Because the root repository base on tensorflow 1.x, run the following command to convert model to tensorflow 2.x

pip install torch==1.4.0
python convert_tf2.py

Before running this script, ensure put necessary files in the folders.

• ./model_pretrained/raw_model checkpoint model that train from this repository
• ./model_pretrained/config_files include config file that contains model architecture (generator and discriminator models). Config file default is Electra small model.