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Update README.md (#1)

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- Update README.md (60a7a10df59d5f85d85315ca298723aeb52af678)


Co-authored-by: Gonçalo Antunes <[email protected]>

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  1. README.md +5 -5
README.md CHANGED
@@ -46,8 +46,8 @@ A standard BERT base for Swedish trained on a variety of sources. Vocabulary siz
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  ```python
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  from transformers import AutoModel,AutoTokenizer
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- tok = AutoTokenizer.from_pretrained('KB/bert-base-swedish-cased')
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- model = AutoModel.from_pretrained('KB/bert-base-swedish-cased')
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  ```
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@@ -58,7 +58,7 @@ This model is fine-tuned on the SUC 3.0 dataset. Using the Huggingface pipeline
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  ```python
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  from transformers import pipeline
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- nlp = pipeline('ner', model='KB/bert-base-swedish-cased-ner', tokenizer='KB/bert-base-swedish-cased-ner')
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  nlp('Idag släpper KB tre språkmodeller.')
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  ```
@@ -109,8 +109,8 @@ The easiest way to do this is, again, using Huggingface Transformers:
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  ```python
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  from transformers import AutoModel,AutoTokenizer
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- tok = AutoTokenizer.from_pretrained('KB/albert-base-swedish-cased-alpha'),
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- model = AutoModel.from_pretrained('KB/albert-base-swedish-cased-alpha')
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  ```
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  ## Acknowledgements ❤️
 
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  ```python
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  from transformers import AutoModel,AutoTokenizer
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+ tok = AutoTokenizer.from_pretrained('KBLab/bert-base-swedish-cased')
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+ model = AutoModel.from_pretrained('KBLab/bert-base-swedish-cased')
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  ```
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  ```python
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  from transformers import pipeline
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+ nlp = pipeline('ner', model='KBLab/bert-base-swedish-cased-ner', tokenizer='KBLab/bert-base-swedish-cased-ner')
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  nlp('Idag släpper KB tre språkmodeller.')
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  ```
 
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  ```python
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  from transformers import AutoModel,AutoTokenizer
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+ tok = AutoTokenizer.from_pretrained('KBLab/albert-base-swedish-cased-alpha'),
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+ model = AutoModel.from_pretrained('KBLab/albert-base-swedish-cased-alpha')
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  ```
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  ## Acknowledgements ❤️