Update README.md

README.md

@@ -12,6 +12,29 @@ An [adapter](https://adapterhub.ml) for the [`allenai/specter2_base`](https://hu
 
 This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
 
+**Aug 2023 Update:**
+1. **The SPECTER 2.0 Base and proximity adapter models have been renamed in Hugging Face based upon usage patterns as follows:**
+
+|Old Name|New Name|
+|--|--|
+|allenai/specter2|[allenai/specter2_base](https://huggingface.co/allenai/specter2_base)|
+|allenai/specter2_proximity|[allenai/specter2](https://huggingface.co/allenai/specter2)|
+
+2. **We have a parallel version (termed [aug2023refresh](https://huggingface.co/allenai/specter2_aug2023refresh)) where the base transformer encoder version is pre-trained on a collection of newer papers (published after 2018).
+   However, for benchmarking purposes, please continue using the current version.**
+
+## SPECTER 2.0
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+SPECTER 2.0 is the successor to [SPECTER](https://huggingface.co/allenai/specter) and is capable of generating task specific embeddings for scientific tasks when paired with [adapters](https://huggingface.co/models?search=allenai/specter-2_).
+This is the base model to be used along with the adapters.
+Given the combination of title and abstract of a scientific paper or a short texual query, the model can be used to generate effective embeddings to be used in downstream applications.
+
+**Note:For general embedding purposes, please use [allenai/specter2](https://huggingface.co/allenai/specter2).**
+
+**To get the best performance on a downstream task type please load the associated adapter with the base model as in the example below.**
+
 ## Usage
 
 First, install `adapter-transformers`:
@@ -30,30 +53,6 @@ model = AutoAdapterModel.from_pretrained("allenai/specter2_base")
 adapter_name = model.load_adapter("allenai/specter2_regression", source="hf", set_active=True)
 ```
 
-## SPECTER 2.0
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-**Aug 2023 Update:**
-1. The SPECTER 2.0 Base and proximity adapter models have been renamed in Hugging Face based upon usage patterns as follows:
-
-|Old Name|New Name|
-|--|--|
-|allenai/specter2|[allenai/specter2_base](https://huggingface.co/allenai/specter2_base)|
-|allenai/specter2_proximity|[allenai/specter2](https://huggingface.co/allenai/specter2)|
-
-2. We have a parallel version (termed [aug2023refresh](https://huggingface.co/allenai/specter2_aug2023refresh)) where the base transformer encoder version is pre-trained on a collection of newer papers (published after 2018).
-   However, for benchmarking purposes, please continue using the current version.
-
-
-
-SPECTER 2.0 is the successor to [SPECTER](https://huggingface.co/allenai/specter) and is capable of generating task specific embeddings for scientific tasks when paired with [adapters](https://huggingface.co/models?search=allenai/specter-2_).
-This is the base model to be used along with the adapters.
-Given the combination of title and abstract of a scientific paper or a short texual query, the model can be used to generate effective embeddings to be used in downstream applications.
-
-**Note:For general embedding purposes, please use [allenai/specter2](https://huggingface.co/allenai/specter2).**
-
-**To get the best performance on a downstream task type please load the associated adapter with the base model as in the example below.**
 
 # Model Details