Update README.md

README.md

@@ -16,7 +16,7 @@ The demo can be found [here](https://huggingface.co/spaces/flax-community/gpt2-i
 You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility:
 ```python
 >>> from transformers import pipeline, set_seed
->>> generator = pipeline('text-generation', model='flax-community/gpt2-small-indonesian')
+>>> generator = pipeline('text-generation', model='flax-community/gpt2-medium-indonesian')
 >>> set_seed(42)
 >>> generator("Sewindu sudah kita tak berjumpa,", max_length=30, num_return_sequences=5)
 
@@ -32,8 +32,8 @@ Tuhan akan memberi lebih dari apa yang kita'}]
 Here is how to use this model to get the features of a given text in PyTorch:
 ```python
 from transformers import GPT2Tokenizer, GPT2Model
-tokenizer = GPT2Tokenizer.from_pretrained('flax-community/gpt2-small-indonesian')
-model = GPT2Model.from_pretrained('flax-community/gpt2-small-indonesian')
+tokenizer = GPT2Tokenizer.from_pretrained('flax-community/gpt2-medium-indonesian')
+model = GPT2Model.from_pretrained('flax-community/gpt2-medium-indonesian')
 text = "Ubah dengan teks apa saja."
 encoded_input = tokenizer(text, return_tensors='pt')
 output = model(**encoded_input)
@@ -42,8 +42,8 @@ output = model(**encoded_input)
 and in TensorFlow:
 ```python
 from transformers import GPT2Tokenizer, TFGPT2Model
-tokenizer = GPT2Tokenizer.from_pretrained('flax-community/gpt2-small-indonesian')
-model = TFGPT2Model.from_pretrained('flax-community/gpt2-small-indonesian')
+tokenizer = GPT2Tokenizer.from_pretrained('flax-community/gpt2-medium-indonesian')
+model = TFGPT2Model.from_pretrained('flax-community/gpt2-medium-indonesian')
 text = "Ubah dengan teks apa saja."
 encoded_input = tokenizer(text, return_tensors='tf')
 output = model(encoded_input)