Update README.md

README.md

@@ -6,16 +6,34 @@ datasets:
 - allenai/scirepeval
 ---
 
-# Adapter `allenai/specter2_aug2023refresh` for `allenai/specter2_aug2023refresh_base`
+## SPECTER2
 
-An [adapter](https://adapterhub.ml) for the allenai/specter2_aug2023refresh_base model that was trained on the [allenai/scirepeval](https://huggingface.co/datasets/allenai/scirepeval/) dataset.
+<!-- Provide a quick summary of what the model is/does. -->
 
-This adapter was created for usage with the **[adapters](https://github.com/adapter-hub/adapters)** library.
+SPECTER2 is a family of models that succeeds [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_).
+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.**
 
 **Dec 2023 Update:**
 
 Model usage updated to be compatible with latest versions of transformers and adapters (newly released update to adapter-transformers) libraries.
 
+**\*\*\*\*\*\*Update\*\*\*\*\*\***
+
+This update introduces a new set of SPECTER2 models with the base transformer encoder pre-trained on an extended citation dataset containing more recent papers.
+For benchmarking purposes please use the existing SPECTER2 [models](https://huggingface.co/allenai/specter2) w/o the **aug2023refresh** suffix.
+
+
+# Adapter `allenai/specter2_aug2023refresh` for `allenai/specter2_aug2023refresh_base`
+
+An [adapter](https://adapterhub.ml) for the allenai/specter2_aug2023refresh_base model that was trained on the [allenai/scirepeval](https://huggingface.co/datasets/allenai/scirepeval/) dataset.
+
+This adapter was created for usage with the **[adapters](https://github.com/adapter-hub/adapters)** library.
+
+
 ## Usage
 
 First, install `adapters`:
@@ -34,20 +52,6 @@ model = AutoAdapterModel.from_pretrained("allenai/specter2_aug2023refresh_base")
 adapter_name = model.load_adapter("allenai/specter2_aug2023refresh", source="hf", set_active=True)
 ```
 
-**\*\*\*\*\*\*Update\*\*\*\*\*\***
-
-This update introduces a new set of SPECTER2 models with the base transformer encoder pre-trained on an extended citation dataset containing more recent papers.
-For benchmarking purposes please use the existing SPECTER2 [models](https://huggingface.co/allenai/specter2) w/o the **aug2023refresh** suffix.
-
-# SPECTER2 (Base)
-SPECTER2 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
 
 ## Model Description
@@ -61,7 +65,8 @@ Task Formats trained on:
 - Proximity
 - Adhoc Search
 
-  
+**This is a retrieval specific adapter. For tasks where given a paper query, other relevant papers have to be retrieved from a corpus, use this adapter to generate the embeddings.**
+
 It builds on the work done in [SciRepEval: A Multi-Format Benchmark for Scientific Document Representations](https://api.semanticscholar.org/CorpusID:254018137) and we evaluate the trained model on this benchmark as well.
 
 
@@ -93,7 +98,7 @@ It builds on the work done in [SciRepEval: A Multi-Format Benchmark for Scientif
 |Classification|[allenai/specter2_aug2023refresh_classification](https://huggingface.co/allenai/specter2_aug2023refresh_classification)|Encode papers to feed into linear classifiers as features|
 |Regression|[allenai/specter2_aug2023refresh_regression](https://huggingface.co/allenai/specter2_aug2023refresh_regression)|Encode papers to feed into linear regressors as features|
 
-*Retrieval model should suffice for downstream task types not mentioned above
+*Proximity model should suffice for downstream task types not mentioned above
 
 ```python
 from transformers import AutoTokenizer