Update README.md

README.md

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