Update README.md

README.md

@@ -6,6 +6,11 @@ language:
 - en
 ---
 
+<!-- Provide a quick summary of what the model is/does. -->
+
+# SPECTER 2.0 (Base)
+
+
 **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:
 
@@ -18,9 +23,7 @@ language:
    However, for benchmarking purposes, please continue using the current version.
 
 
-<!-- Provide a quick summary of what the model is/does. -->
 
-# SPECTER 2.0 (Base)
 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.
@@ -39,7 +42,7 @@ Post that it is trained with additionally attached task format specific adapter
 Task Formats trained on:
 - Classification
 - Regression
-- Proximity
+- Proximity (Retrieval)
 - Adhoc Search
 
   
@@ -69,12 +72,12 @@ It builds on the work done in [SciRepEval: A Multi-Format Benchmark for Scientif
 
 |Model|Name and HF link|Description|
 |--|--|--|
-|Retrieval*|[allenai/specter2_proximity](https://huggingface.co/allenai/specter2)|Encode papers as queries and candidates eg. Link Prediction, Nearest Neighbor Search|
-|Adhoc Query|[allenai/specter2_adhoc_query](https://huggingface.co/allenai/specter2_adhoc_query)|Encode short raw text queries for search tasks. (Candidate papers can be encoded with proximity)|
+|Proximity*|[allenai/specter2](https://huggingface.co/allenai/specter2)|Encode papers as queries and candidates eg. Link Prediction, Nearest Neighbor Search|
+|Adhoc Query|[allenai/specter2_adhoc_query](https://huggingface.co/allenai/specter2_adhoc_query)|Encode short raw text queries for search tasks. (Candidate papers can be encoded with the proximity adapter)|
 |Classification|[allenai/specter2_classification](https://huggingface.co/allenai/specter2_classification)|Encode papers to feed into linear classifiers as features|
 |Regression|[allenai/specter2_regression](https://huggingface.co/allenai/specter2_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, AutoModel
@@ -86,7 +89,7 @@ tokenizer = AutoTokenizer.from_pretrained('allenai/specter2_base')
 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="proximity", set_active=True)
+model.load_adapter("allenai/specter2", source="hf", load_as="proximity", set_active=True)
 #other possibilities: allenai/specter2_<classification|regression|adhoc_query>
 
 papers = [{'title': 'BERT', 'abstract': 'We introduce a new language representation model called BERT'},