Migrate model card from transformers-repo

Read announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/monologg/koelectra-base-discriminator/README.md

README.md

@@ -0,0 +1,52 @@
+---
+language: ko
+---
+
+# KoELECTRA (Base Discriminator)
+
+Pretrained ELECTRA Language Model for Korean (`koelectra-base-discriminator`)
+
+For more detail, please see [original repository](https://github.com/monologg/KoELECTRA/blob/master/README_EN.md).
+
+## Usage
+
+### Load model and tokenizer
+
+```python
+>>> from transformers import ElectraModel, ElectraTokenizer
+
+>>> model = ElectraModel.from_pretrained("monologg/koelectra-base-discriminator")
+>>> tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-base-discriminator")
+```
+
+### Tokenizer example
+
+```python
+>>> from transformers import ElectraTokenizer
+>>> tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-base-discriminator")
+>>> tokenizer.tokenize("[CLS] 한국어 ELECTRA를 공유합니다. [SEP]")
+['[CLS]', '한국어', 'E', '##L', '##EC', '##T', '##RA', '##를', '공유', '##합니다', '.', '[SEP]']
+>>> tokenizer.convert_tokens_to_ids(['[CLS]', '한국어', 'E', '##L', '##EC', '##T', '##RA', '##를', '공유', '##합니다', '.', '[SEP]'])
+[2, 18429, 41, 6240, 15229, 6204, 20894, 5689, 12622, 10690, 18, 3]
+```
+
+## Example using ElectraForPreTraining
+
+```python
+import torch
+from transformers import ElectraForPreTraining, ElectraTokenizer
+
+discriminator = ElectraForPreTraining.from_pretrained("monologg/koelectra-base-discriminator")
+tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-base-discriminator")
+
+sentence = "나는 방금 밥을 먹었다."
+fake_sentence = "나는 내일 밥을 먹었다."
+
+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()[1:-1])))
+```