Update README.md

README.md

@@ -25,7 +25,7 @@ You can use this model directly with a pipeline for masked language modeling:
 
 ```python
 from transformers import pipeline
-summarizer = pipeline(task="summarization", model="turingmachine/hupd-t5-small")
+summarizer = pipeline(task="summarization", model="hupd/hupd-t5-small")
 
 TEXT = "1. An optical coherent receiver for an optical communication network, said optical coherent receiver being configured to receive a modulated optical signal and to process said modulated optical signal for generating an in-phase component and a quadrature component, said in-phase component and said quadrature component being electrical signals, said optical coherent receiver comprising a power adjuster in turn comprising: a multiplying unit configured to multiply said in-phase component by an in-phase gain thereby providing a power-adjusted in-phase component, and to multiply said quadrature component by a quadrature gain thereby providing a power-adjusted quadrature component; and a digital circuit connected between output and input of said multiplying unit and configured to compute: a common gain indicative of a sum of a power of said power-adjusted in-phase component and a power of said power-adjusted quadrature component, and a differential gain indicative of a difference between said power of said power-adjusted in-phase component and said power of said power-adjusted quadrature component; and said in-phase gain as a product between said common gain and said differential gain, and said quadrature gain as a ratio between said common gain and said differential gain. 2. An optical coherent receiver according to claim 1, wherein it further comprises an analog-to-digital unit connected at the input of said power adjuster, said analog-to-digital unit being configured to ..."
 
@@ -44,8 +44,8 @@ import torch
 from transformers import AutoTokenizer, AutoModelWithLMHead
 # cuda/cpu
 device = 'cuda' if torch.cuda.is_available() else 'cpu'
-tokenizer = AutoTokenizer.from_pretrained("turingmachine/hupd-t5-small")
-model = AutoModelWithLMHead.from_pretrained("turingmachine/hupd-t5-small").to(device)
+tokenizer = AutoTokenizer.from_pretrained("hupd/hupd-t5-small")
+model = AutoModelWithLMHead.from_pretrained("hupd/hupd-t5-small").to(device)
 
 inputs = tokenizer(TEXT, return_tensors="pt").to(device)