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Update README.md

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@@ -26,8 +26,8 @@ Now, the adapter can be loaded and activated like this:
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  ```python
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  from transformers import AutoAdapterModel
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- model = AutoAdapterModel.from_pretrained("allenai/specter2")
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- adapter_name = model.load_adapter("allenai/specter2_proximity", source="hf", set_active=True)
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  ```
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  ## SPECTER 2.0
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@@ -90,13 +90,13 @@ It builds on the work done in [SciRepEval: A Multi-Format Benchmark for Scientif
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  from transformers import AutoTokenizer, AutoModel
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  # load model and tokenizer
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- tokenizer = AutoTokenizer.from_pretrained('allenai/specter2')
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  #load base model
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- model = AutoModel.from_pretrained('allenai/specter2')
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  #load the adapter(s) as per the required task, provide an identifier for the adapter in load_as argument and activate it
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- model.load_adapter("allenai/specter2_proximity", source="hf", load_as="specter2_proximity", set_active=True)
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  papers = [{'title': 'BERT', 'abstract': 'We introduce a new language representation model called BERT'},
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  {'title': 'Attention is all you need', 'abstract': ' The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'}]
 
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  ```python
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  from transformers import AutoAdapterModel
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+ model = AutoAdapterModel.from_pretrained("allenai/specter2_base")
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+ adapter_name = model.load_adapter("allenai/specter2", source="hf", set_active=True)
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  ```
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  ## SPECTER 2.0
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  from transformers import AutoTokenizer, AutoModel
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  # load model and tokenizer
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+ tokenizer = AutoTokenizer.from_pretrained('allenai/specter2_base')
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  #load base model
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+ model = AutoModel.from_pretrained('allenai/specter2_base')
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  #load the adapter(s) as per the required task, provide an identifier for the adapter in load_as argument and activate it
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+ model.load_adapter("allenai/specter2", source="hf", load_as="specter2", set_active=True)
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  papers = [{'title': 'BERT', 'abstract': 'We introduce a new language representation model called BERT'},
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  {'title': 'Attention is all you need', 'abstract': ' The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'}]