Update README.md

README.md

@@ -12,9 +12,9 @@ set a seed for reproducibility:
 
 ```python
 >>> from transformers import pipeline, set_seed
->>> from transformers import BioGptTokenizer, BioGptLMHeadModel
->>> model = BioGptLMHeadModel.from_pretrained("kamalkraj/biogpt")
->>> tokenizer = BioGptTokenizer.from_pretrained("kamalkraj/biogpt")
+>>> from transformers import BioGptTokenizer, BioGptForCausalLM
+>>> model = BioGptForCausalLM.from_pretrained("microsoft/biogpt")
+>>> tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt")
 >>> generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
 >>> set_seed(42)
 >>> generator("COVID-19 is", max_length=20, num_return_sequences=5, do_sample=True)
@@ -28,9 +28,9 @@ set a seed for reproducibility:
 Here is how to use this model to get the features of a given text in PyTorch:
 
 ```python
-from transformers import BioGptTokenizer, BioGptModel
-tokenizer = BioGptTokenizer.from_pretrained("kamalkraj/biogpt")
-model = BioGptModel.from_pretrained("kamalkraj/biogpt")
+from transformers import BioGptTokenizer, BioGptForCausalLM
+tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt")
+model = BioGptForCausalLM.from_pretrained("microsoft/biogpt")
 text = "Replace me by any text you'd like."
 encoded_input = tokenizer(text, return_tensors='pt')
 output = model(**encoded_input)
@@ -40,10 +40,10 @@ Beam-search decoding:
 
 ```python
 import torch
-from transformers import BioGptTokenizer, BioGptLMHeadModel, set_seed
+from transformers import BioGptTokenizer, BioGptForCausalLM, set_seed
 
-tokenizer = BioGptTokenizer.from_pretrained("kamalkraj/biogpt")
-model = BioGptLMHeadModel.from_pretrained("kamalkraj/biogpt")
+tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt")
+model = BioGptForCausalLM.from_pretrained("microsoft/biogpt")
 
 sentence = "COVID-19 is"
 inputs = tokenizer(sentence, return_tensors="pt")