Update README.md

updated name in tokenizer and model

README.md

@@ -46,8 +46,8 @@ A standard BERT base for Swedish trained on a variety of sources. Vocabulary siz
 ```python
 from transformers import AutoModel,AutoTokenizer
 
-tok = AutoTokenizer.from_pretrained('KB/bert-base-swedish-cased')
-model = AutoModel.from_pretrained('KB/bert-base-swedish-cased')
+tok = AutoTokenizer.from_pretrained('KBLab/bert-base-swedish-cased')
+model = AutoModel.from_pretrained('KBLab/bert-base-swedish-cased')
 ```
 
 
@@ -58,7 +58,7 @@ This model is fine-tuned on the SUC 3.0 dataset. Using the Huggingface pipeline
 ```python
 from transformers import pipeline
 
-nlp = pipeline('ner', model='KB/bert-base-swedish-cased-ner', tokenizer='KB/bert-base-swedish-cased-ner')
+nlp = pipeline('ner', model='KBLab/bert-base-swedish-cased-ner', tokenizer='KBLab/bert-base-swedish-cased-ner')
 
 nlp('Idag släpper KB tre språkmodeller.')
 ```
@@ -109,8 +109,8 @@ The easiest way to do this is, again, using Huggingface Transformers:
 ```python
 from transformers import AutoModel,AutoTokenizer
 
-tok = AutoTokenizer.from_pretrained('KB/albert-base-swedish-cased-alpha'),
-model = AutoModel.from_pretrained('KB/albert-base-swedish-cased-alpha')
+tok = AutoTokenizer.from_pretrained('KBLab/albert-base-swedish-cased-alpha'),
+model = AutoModel.from_pretrained('KBLab/albert-base-swedish-cased-alpha')
 ```
 
 ## Acknowledgements ❤️