
# Bert base model for Korean

* 70GB Korean text dataset and 42000 lower-cased subwords are used
* Check the model performance and other language models for Korean in [github](https://github.com/kiyoungkim1/LM-kor)

```python
from transformers import BertTokenizerFast, GPT2LMHeadModel
tokenizer_gpt3 = BertTokenizerFast.from_pretrained("kykim/gpt3-kor-small_based_on_gpt2")
input_ids = tokenizer_gpt3.encode("text to tokenize")[1:]  # remove cls token
        
model_gpt3 = GPT2LMHeadModel.from_pretrained("kykim/gpt3-kor-small_based_on_gpt2")
```
{"language": "ko", "tags": ["text-generation"]}
transformers pytorch tf jax gpt2 text-generation ko autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #ko #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 


# Bert base model for Korean

* 70GB Korean text dataset and 42000 lower-cased subwords are used
* Check the model performance and other language models for Korean in github


# Bert base model for Korean

* 70GB Korean text dataset and 42000 lower-cased subwords are used
* Check the model performance and other language models for Korean in github
TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #ko #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 # Bert base model for Korean

* 70GB Korean text dataset and 42000 lower-cased subwords are used
* Check the model performance and other language models for Korean in github
passage: TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #ko #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
# Bert base model for Korean

* 70GB Korean text dataset and 42000 lower-cased subwords are used
* Check the model performance and other language models for Korean in github