Model cards for finance-koelectra models (#5313)

* Add finance-koelectra readme card

* Add finance-koelectra readme card

* Add finance-koelectra readme card

* Add finance-koelectra readme card

model_cards/krevas/finance-koelectra-base-discriminator/README.md

+---
+language: korean
+---
+
+# 📈 Financial Korean ELECTRA model
+
+Pretrained ELECTRA Language Model for Korean (`finance-koelectra-base-discriminator`)
+
+> ELECTRA is a new method for self-supervised language representation learning. It can be used to
+> pre-train transformer networks using relatively little compute. ELECTRA models are trained to
+> distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to
+> the discriminator of a GAN.
+
+More details about ELECTRA can be found in the [ICLR paper](https://openreview.net/forum?id=r1xMH1BtvB)
+or in the [official ELECTRA repository](https://github.com/google-research/electra) on GitHub.
+
+## Stats
+
+The current version of the model is trained on a financial news data of Naver news.
+
+The final training corpus has a size of 25GB and 2.3B tokens.
+
+This model was trained a cased model on a TITAN RTX for 500k steps.
+
+## Usage
+
+```python
+from transformers import ElectraForPreTraining, ElectraTokenizer
+import torch
+discriminator = ElectraForPreTraining.from_pretrained("krevas/finance-koelectra-base-discriminator")
+tokenizer = ElectraTokenizer.from_pretrained("krevas/finance-koelectra-base-discriminator")
+sentence = "내일 해당 종목이 대폭 상승할 것이다"
+fake_sentence = "내일 해당 종목이 맛있게 상승할 것이다"
+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("%7s" % token, end="") for token in fake_tokens]
+[print("%7s" % int(prediction), end="") for prediction in predictions.tolist()[1:-1]]
+print("fake token : %s" % fake_tokens[predictions.tolist()[1:-1].index(1)])
+```
+
+# Huggingface model hub
+
+All models are available on the [Huggingface model hub](https://huggingface.co/krevas).

model_cards/krevas/finance-koelectra-base-generator/README.md

+---
+language: korean
+---
+
+# 📈 Financial Korean ELECTRA model
+
+Pretrained ELECTRA Language Model for Korean (`finance-koelectra-base-generator`)
+
+> ELECTRA is a new method for self-supervised language representation learning. It can be used to
+> pre-train transformer networks using relatively little compute. ELECTRA models are trained to
+> distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to
+> the discriminator of a GAN.
+
+More details about ELECTRA can be found in the [ICLR paper](https://openreview.net/forum?id=r1xMH1BtvB)
+or in the [official ELECTRA repository](https://github.com/google-research/electra) on GitHub.
+
+## Stats
+
+The current version of the model is trained on a financial news data of Naver news.
+
+The final training corpus has a size of 25GB and 2.3B tokens.
+
+This model was trained a cased model on a TITAN RTX for 500k steps.
+
+## Usage
+
+```python
+from transformers import pipeline
+
+fill_mask = pipeline(
+            "fill-mask",
+            model="krevas/finance-koelectra-base-generator",
+            tokenizer="krevas/finance-koelectra-base-generator"
+            )
+
+print(fill_mask(f"내일 해당 종목이 대폭 {fill_mask.tokenizer.mask_token}할 것이다."))
+```
+
+# Huggingface model hub
+
+All models are available on the [Huggingface model hub](https://huggingface.co/krevas).

model_cards/krevas/finance-koelectra-small-discriminator/README.md

+---
+language: korean
+---
+
+# 📈 Financial Korean ELECTRA model
+
+Pretrained ELECTRA Language Model for Korean (`finance-koelectra-small-discriminator`)
+
+> ELECTRA is a new method for self-supervised language representation learning. It can be used to
+> pre-train transformer networks using relatively little compute. ELECTRA models are trained to
+> distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to
+> the discriminator of a GAN.
+
+More details about ELECTRA can be found in the [ICLR paper](https://openreview.net/forum?id=r1xMH1BtvB)
+or in the [official ELECTRA repository](https://github.com/google-research/electra) on GitHub.
+
+## Stats
+
+The current version of the model is trained on a financial news data of Naver news.
+
+The final training corpus has a size of 25GB and 2.3B tokens.
+
+This model was trained a cased model on a TITAN RTX for 500k steps.
+
+## Usage
+
+```python
+from transformers import ElectraForPreTraining, ElectraTokenizer
+import torch
+discriminator = ElectraForPreTraining.from_pretrained("krevas/finance-koelectra-small-discriminator")
+tokenizer = ElectraTokenizer.from_pretrained("krevas/finance-koelectra-small-discriminator")
+sentence = "내일 해당 종목이 대폭 상승할 것이다"
+fake_sentence = "내일 해당 종목이 맛있게 상승할 것이다"
+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("%7s" % token, end="") for token in fake_tokens]
+[print("%7s" % int(prediction), end="") for prediction in predictions.tolist()[1:-1]]
+print("fake token : %s" % fake_tokens[predictions.tolist()[1:-1].index(1)])
+```
+
+# Huggingface model hub
+
+All models are available on the [Huggingface model hub](https://huggingface.co/krevas).

model_cards/krevas/finance-koelectra-small-generator/README.md

+---
+language: korean
+---
+
+# 📈 Financial Korean ELECTRA model
+
+Pretrained ELECTRA Language Model for Korean (`finance-koelectra-small-generator`)
+
+> ELECTRA is a new method for self-supervised language representation learning. It can be used to
+> pre-train transformer networks using relatively little compute. ELECTRA models are trained to
+> distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to
+> the discriminator of a GAN.
+
+More details about ELECTRA can be found in the [ICLR paper](https://openreview.net/forum?id=r1xMH1BtvB)
+or in the [official ELECTRA repository](https://github.com/google-research/electra) on GitHub.
+
+## Stats
+
+The current version of the model is trained on a financial news data of Naver news.
+
+The final training corpus has a size of 25GB and 2.3B tokens.
+
+This model was trained a cased model on a TITAN RTX for 500k steps.
+
+## Usage
+
+```python
+from transformers import pipeline
+
+fill_mask = pipeline(
+            "fill-mask",
+            model="krevas/finance-koelectra-small-generator",
+            tokenizer="krevas/finance-koelectra-small-generator"
+            )
+
+print(fill_mask(f"내일 해당 종목이 대폭 {fill_mask.tokenizer.mask_token}할 것이다."))
+```
+
+# Huggingface model hub
+
+All models are available on the [Huggingface model hub](https://huggingface.co/krevas).