Create README.md

README.md

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+Pretrained ELECTRA Language Model for Korean (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])))
+
+```