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ifuseok/
bw-electra-base-discriminator

Name: bw-electra-base-discriminator
Availability: InStock

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Pretrained ELECTRA Language Model for Korean by bigwaveAI (bw-electra-base-discriminator)

Usage

Load Model and Tokenizer

python

from transformers import ElectraModel,TFElectraModel,ElectraTokenizer
# tensorflow
model = TFElectraModel.from_pretrained("ifuseok/bw-electra-base-discriminator")
# torch
#model = ElectraModel.from_pretrained("ifuseok/bw-electra-base-discriminator",from_tf=True)
tokenizer  = ElectraTokenizer.from_pretrained("ifuseok/bw-electra-base-discriminator",do_lower)

Tokenizer example

python

from transformers import ElectraTokenizer
tokenizer = ElectraTokenizer.from_pretrained("ifuseok/bw-electra-base-discriminator")
tokenizer.tokenize("[CLS] Big Wave ELECTRA 모델을 공개합니다. [SEP]")

Example using ElectraForPreTraining(Torch)

python

import torch
from transformers import ElectraForPreTraining, ElectraTokenizer

discriminator = ElectraForPreTraining.from_pretrained("ifuseok/bw-electra-base-discriminator",from_tf=True)
tokenizer = ElectraTokenizer.from_pretrained("ifuseok/bw-electra-base-discriminator",do_lower_case=False)

sentence = "아무것도 하기가 싫다."
fake_sentence = "아무것도 하기가 좋다."

fake_tokens = tokenizer.tokenize(fake_sentence)
fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt")

discriminator_outputs = discriminator(fake_inputs)
predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2)

print(list(zip(fake_tokens, predictions.tolist()[0][1:-1])))

Example using ElectraForPreTraining(Tensorflow)

python

import tensorflow as tf
from transformers import TFElectraForPreTraining, ElectraTokenizer

discriminator = TFElectraForPreTraining.from_pretrained("ifuseok/bw-electra-base-discriminator" )
tokenizer = ElectraTokenizer.from_pretrained("ifuseok/bw-electra-base-discriminator", use_auth_token=access_token
                                            ,do_lower_case=False)

sentence = "아무것도 하기가 싫다."
fake_sentence = "아무것도 하기가 좋다."

fake_tokens = tokenizer.tokenize(fake_sentence)
fake_inputs = tokenizer.encode(fake_sentence, return_tensors="tf")

discriminator_outputs = discriminator(fake_inputs)
predictions = tf.round((tf.sign(discriminator_outputs[0]) + 1)/2).numpy()

print(list(zip(fake_tokens, predictions.tolist()[0][1:-1])))